Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in recommender systems in industry, owing to its strength in user interest modeling and ease in online personalization. By constructing a user’s profile with the items that the user has consumed, ICF recommends items that are similar to the user’s profile. With the prevalence of machine learning in recent years, significant processes have been made for ICF by learning item similarity (or representation) from data. Nevertheless, we argue that most existing works have only considered linear and shallow relationship between items, which are insufficient to capture the complicated decision-making process of users. In this work, we propose a more expressive ICF solution by accounting for the nonlinear and higher-order relationship among items. Going beyond modeling only the second-order interaction (e.g. similarity) between two items, we additionally consider the interaction among all interacted item pairs by using nonlinear neural networks. Through this way, we can effectively model the higher-order relationship among items, capturing more complicated effects in user decision-making. For example, it can differentiate which historical itemsets in a user’s profile are more important in affecting the user to make a purchase decision on an item. We treat this solution as a deep variant of ICF, thus term it as DeepICF. To justify our proposal, we perform empirical studies on two public datasets from MovieLens and Pinterest. Extensive experiments verify the highly positive effect of higher-order item interaction modeling with nonlinear neural networks. Moreover, we demonstrate that by more fine-grained second-order interaction modeling with attention network, the performance of our DeepICF method can be further improved.
Gaussian-Induced Convolution for Graphs
Learning representation on graph plays a crucial role in numerous tasks of pattern recognition. Different from grid-shaped images/videos, on which local convolution kernels can be lattices, however, graphs are fully coordinate-free on vertices and edges. In this work, we propose a Gaussian-induced convolution (GIC) framework to conduct local convolution filtering on irregular graphs. Specifically, an edge-induced Gaussian mixture model is designed to encode variations of subgraph region by integrating edge information into weighted Gaussian models, each of which implicitly characterizes one component of subgraph variations. In order to coarsen a graph, we derive a vertex-induced Gaussian mixture model to cluster vertices dynamically according to the connection of edges, which is approximately equivalent to the weighted graph cut. We conduct our multi-layer graph convolution network on several public datasets of graph classification. The extensive experiments demonstrate that our GIC is effective and can achieve the state-of-the-art results.
Fast Matrix Factorization with Non-Uniform Weights on Missing Data
Matrix factorization (MF) has been widely used to discover the low-rank structure and to predict the missing entries of data matrix. In many real-world learning systems, the data matrix can be very high-dimensional but sparse. This poses an imbalanced learning problem, since the scale of missing entries is usually much larger than that of observed entries, but they cannot be ignored due to the valuable negative signal. For efficiency concern, existing work typically applies a uniform weight on missing entries to allow a fast learning algorithm. However, this simplification will decrease modeling fidelity, resulting in suboptimal performance for downstream applications. In this work, we weight the missing data non-uniformly, and more generically, we allow any weighting strategy on the missing data. To address the efficiency challenge, we propose a fast learning method, for which the time complexity is determined by the number of observed entries in the data matrix, rather than the matrix size. The key idea is two-fold: 1) we apply truncated SVD on the weight matrix to get a more compact representation of the weights, and 2) we learn MF parameters with element-wise alternating least squares (eALS) and memorize the key intermediate variables to avoid repeating computations that are unnecessary. We conduct extensive experiments on two recommendation benchmarks, demonstrating the correctness, efficiency, and effectiveness of our fast eALS method.
An Optimal Control View of Adversarial Machine Learning
I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary’s goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, including test-item attacks, training-data poisoning, and adversarial reward shaping. The view encourages adversarial machine learning researcher to utilize advances in control theory and reinforcement learning.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end Structure-Aware Convolutional Networks (SACN) that take the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and relation types. It has learnable weights that collect adaptive amount of information from neighboring graph nodes, resulting in more accurate embeddings of graph nodes. In addition, the node attributes are added as the nodes and are easily integrated into the WGCN. The decoder Conv-TransE extends the state-of-the-art ConvE to be translational between entities and relations while keeps the state-of-the-art performance as ConvE. We demonstrate the effectiveness of our proposed SACN model on standard FB15k-237 and WN18RR datasets, and present about 10% relative improvement over the state-of-the-art ConvE in terms of HITS@1, HITS@3 and HITS@10.
ReDecode Framework for Iterative Improvement in Paraphrase Generation
Generating paraphrases, that is, different variations of a sentence conveying the same meaning, is an important yet challenging task in NLP. Automatically generating paraphrases has its utility in many NLP tasks like question answering, information retrieval, conversational systems to name a few. In this paper, we introduce iterative refinement of generated paraphrases within VAE based generation framework. Current sequence generation models lack the capability to (1) make improvements once the sentence is generated; (2) rectify errors made while decoding. We propose a technique to iteratively refine the output using multiple decoders, each one attending on the output sentence generated by the previous decoder. We improve current state of the art results significantly – with over 9% and 28% absolute increase in METEOR scores on Quora question pairs and MSCOCO datasets respectively. We also show qualitatively through examples that our re-decoding approach generates better paraphrases compared to a single decoder by rectifying errors and making improvements in paraphrase structure, inducing variations and introducing new but semantically coherent information.
Computational Complexity Analysis of Genetic Programming
Genetic Programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way. Rather than identifying the optimum of a function as in more traditional evolutionary optimization, the aim of GP is to evolve computer programs with a given functionality. A population of programs is evolved using variation operators inspired by Darwinian evolution (crossover and mutation) and natural selection principles to guide the search process towards better programs. While many GP applications have produced human competitive results, the theoretical understanding of what problem characteristics and algorithm properties allow GP to be effective is comparatively limited. Compared to traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable length representation of candidate programs, and the difficulty of evaluating their quality efficiently. Such difficulties considerably impact the runtime analysis of GP where space complexity also comes into play. As a result initial complexity analyses of GP focused on restricted settings such as evolving trees with given structures or estimating the quality of solutions using only a small polynomial number of input/output examples. However, the first runtime analyses concerning GP applications for evolving proper functions with defined input/output behavior have recently appeared. In this chapter, we present an overview of the state-of-the-art.
RADS: Real-time Anomaly Detection System for Cloud Data Centres
Cybersecurity attacks in Cloud data centres are increasing alongside the growth of the Cloud services market. Existing research proposes a number of anomaly detection systems for detecting such attacks. However, these systems encounter a number of challenges, specifically due to the unknown behaviour of the attacks and the occurrence of genuine Cloud workload spikes, which must be distinguished from attacks. In this paper, we discuss these challenges and investigate the issues with the existing Cloud anomaly detection approaches. Then, we propose a Real-time Anomaly Detection System (RADS) for Cloud data centres, which uses a one class classification algorithm and a window-based time series analysis to address the challenges. Specifically, RADS can detect VM-level anomalies occurring due to DDoS and cryptomining attacks. We evaluate the performance of RADS by running lab-based experiments and by using real-world Cloud workload traces. Evaluation results demonstrate that RADS can achieve 90-95% accuracy with a low false positive rate of 0-3%. The results further reveal that RADS experiences fewer false positives when using its window-based time series analysis in comparison to using state-of-the-art average or entropy based analysis.
Anomaly Detection and Correction in Large Labeled Bipartite Graphs
Binary classification problems can be naturally modeled as bipartite graphs, where we attempt to classify right nodes based on their left adjacencies. We consider the case of labeled bipartite graphs in which some labels and edges are not trustworthy. Our goal is to reduce noise by identifying and fixing these labels and edges. We first propose a geometric technique for generating random graph instances with untrustworthy labels and analyze the resulting graph properties. We focus on generating graphs which reflect real-world data, where degree and label frequencies follow power law distributions. We review several algorithms for the problem of detection and correction, proposing novel extensions and making observations specific to the bipartite case. These algorithms range from math programming algorithms to discrete combinatorial algorithms to Bayesian approximation algorithms to machine learning algorithms. We compare the performance of all these algorithms using several metrics and, based on our observations, identify the relative strengths and weaknesses of each individual algorithm.
An Interpretable Generative Model for Handwritten Digit Image Synthesis
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are trained by backpropagation (BP). The training process is complex and the underlying mechanism is difficult to explain. We propose an interpretable multi-stage PCA method to achieve the same goal and use handwritten digit images synthesis as an illustrative example. First, we derive principal-component-analysis-based (PCA-based) transform kernels at each stage based on the covariance of its inputs. This results in a sequence of transforms that convert input images of correlated pixels to spectral vectors of uncorrelated components. In other words, it is a whitening process. Then, we can synthesize an image based on random vectors and multi-stage transform kernels through a coloring process. The generative model is a feedforward (FF) design since no BP is used in model parameter determination. Its design complexity is significantly lower, and the whole design process is explainable. Finally, we design an FF generative model using the MNIST dataset, compare synthesis results with those obtained by state-of-the-art GAN and VAE methods, and show that the proposed generative model achieves comparable performance.
A Model-Centric Analysis of Openness, Replication, and Reproducibility
The literature on the reproducibility crisis presents several putative causes for the proliferation of irreproducible results, including HARKing, p-hacking and publication bias. Without a theory of reproducibility, however, it is difficult to determine whether these putative causes can explain most irreproducible results. Drawing from an historically informed conception of science that is open and collaborative, we identify the components of an idealized experiment and analyze these components as a precursor to develop such a theory. Openness, we suggest, has long been intuitively proposed as a solution to irreproducibility. However, this intuition has not been validated in a theoretical framework. Our concern is that the under-theorizing of these concepts can lead to flawed inferences about the (in)validity of experimental results or integrity of individual scientists. We use probabilistic arguments and examine how openness of experimental components relates to reproducibility of results. We show that there are some impediments to obtaining reproducible results that precede many of the causes often cited in literature on the reproducibility crisis. For example, even if erroneous practices such as HARKing, p-hacking, and publication bias were absent at the individual and system level, reproducibility may still not be guaranteed.
Adversarial Learning-Based On-Line Anomaly Monitoring for Assured Autonomy
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator commands to sensor inputs, system-focused anomaly detection (SFAM). CFAM is an image conditioned energy based generative adversarial network (EBGAN) in which the energy based discriminator distinguishes between proper and anomalous actuator commands. SFAM is based on an action condition video prediction framework to detect anomalies between predicted and observed temporal evolution of sensor data. We demonstrate the effectiveness of the approach on our autonomous ground vehicle for indoor environments and on Udacity dataset for outdoor environments.
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user’s interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.
Recent Research Advances on Interactive Machine Learning
Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.
An Easy Implementation of CV-TMLE
In the world of targeted learning, cross-validated targeted maximum likelihood estimators, CV-TMLE \parencite{Zheng:2010aa}, has a distinct advantage over TMLE \parencite{Laan:2006aa} in that one less condition is required of CV-TMLE in order to achieve asymptotic efficiency in the nonparametric or semiparametric settings. CV-TMLE as originally formulated, consists of averaging usually 10 (for 10-fold cross-validation) parameter estimates, each of which is performed on a validation set separate from where the initial fit was trained. The targeting step is usually performed as a pooled regression over all validation folds but in each fold, we separately evaluate any means as well as the parameter estimate. One nice thing about CV-TMLE, is that we average 10 plug-in estimates so the plug-in quality of preserving the natural parameter bounds is respected. Our adjustment of this procedure also preserves the plug-in characteristic as well as avoids the donsker condtion. The advantage of our procedure is the implementation of the targeting is identical to that of a regular TMLE, once all the validation set initial predictions have been formed. In short, we stack the validation set predictions and pretend as if we have a regular TMLE, which is not necessarily quite a plug-in estimator on each fold but overall will perform asymptotically the same and might have some slight advantage, a subject for future research. In the case of average treatment effect, treatment specific mean and mean outcome under a stochastic intervention, the procedure overlaps exactly with the originally formulated CV-TMLE with a pooled regression for the targeting.
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders
Anomaly detection using dimensionality reduction has been an essential technique for monitoring multidimensional data. Although deep learning-based methods have been well studied for their remarkable detection performance, their interpretability is still a problem. In this paper, we propose a novel algorithm for estimating the dimensions contributing to the detected anomalies by using variational autoencoders (VAEs). Our algorithm is based on an approximative probabilistic model that considers the existence of anomalies in the data, and by maximizing the log-likelihood, we estimate which dimensions contribute to determining data as an anomaly. The experiments results with benchmark datasets show that our algorithm extracts the contributing dimensions more accurately than baseline methods.
Differentiating Concepts and Instances for Knowledge Graph Embedding
Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from
h…/ github.com/davidlvxin/TransC.
A Review for Weighted MinHash Algorithms
Data similarity (or distance) computation is a fundamental research topic which underpins many high-level applications based on similarity measures in machine learning and data mining. However, in large-scale real-world scenarios, the exact similarity computation has become daunting due to ‘3V’ nature (volume, velocity and variety) of big data. In such cases, the hashing techniques have been verified to efficiently conduct similarity estimation in terms of both theory and practice. Currently, MinHash is a popular technique for efficiently estimating the Jaccard similarity of binary sets and furthermore, weighted MinHash is generalized to estimate the generalized Jaccard similarity of weighted sets. This review focuses on categorizing and discussing the existing works of weighted MinHash algorithms. In this review, we mainly categorize the Weighted MinHash algorithms into quantization-based approaches, ‘active index’-based ones and others, and show the evolution and inherent connection of the weighted MinHash algorithms, from the integer weighted MinHash algorithms to real-valued weighted MinHash ones (particularly the Consistent Weighted Sampling scheme). Also, we have developed a python toolbox for the algorithms, and released it in our github. Based on the toolbox, we experimentally conduct a comprehensive comparative study of the standard MinHash algorithm and the weighted MinHash ones.
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial networks (GANs) to model label dependency. The discriminator learns to model label dependency by discriminating real and generated label sets. To fool the discriminator, the classifier, or generator, learns to generate label sets with dependencies close to real data. Extensive experiments and comparisons on two large-scale image classification benchmark datasets (MS-COCO and NUS-WIDE) show that the discriminator improves generalization ability for different kinds of models
Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
We suggest a new methodology for analysis and approximate computations of the Partition Functions (PF) of Graphical Models (GM) in the Normal Factor Graph representation that combines the gauge transformation (GT) technique from (Chertkov, Chernyak 2006) with the technique developed in (Straszak, Vishnoi 2017) based on the recent progress in the field of real stable polynomials. We show that GTs (while keeping PF invariant) allow representation of PF as a sum of polynomials of variables associated with edges of the graph. A special belief propagation (BP) gauge makes a single out term of the series least sensitive to variations then resulting in the loop series for PF introduced in (Chertkov, Chernyak 2006). In addition to restating the known results in the polynomial form, we also discover a new relation between the computationally tractable BP term (single out term of the loop series evaluated at the BP gauge) and the PF: sequential application of differential operators, each associated with an edge of the graph, to the BP polynomial results in the PF. Each term in the sequence corresponds to a BP polynomial of a modified GM derived by contraction of an edge. Even though complexity of computing factors in the derived GMs grow exponentially with the number of eliminated edges, polynomials associated with the new factors remain real stable if the original factors have this property. Moreover, we show that BP estimations for the PF do not decrease with eliminations, thus resulting overall in the proof that the BP solution of the original GM gives a lower bound for PF. The proof extends results of (Straszak, Vishnoi 2017) from bipartite to general graphs, however, it is limited to the case when the BP solution is feasible.
• NExUS: Bayesian simultaneous network estimation across unequal sample sizes
• Relation of Web Service Orchestration, Abstract Process, Web Service and Choreography
• Towards time-varying proximal dynamics in Multi-Agent Network Games
• HSD-CNN: Hierarchically self decomposing CNN architecture using class specific filter sensitivity analysis
• An Initial Attempt of Combining Visual Selective Attention with Deep Reinforcement Learning
• Learning Groupwise Scoring Functions Using Deep Neural Networks
• Optimal Spectral Initialization for Signal Recovery with Applications to Phase Retrieval
• About the ordinances of the vectors of the $n$-dimensional Boolean cube in accordance with their weights
• When Locally Linear Embedding Hits Boundary
• Faster sublinear approximations of $k$-cliques for low arboricity graphs
• Dynamics of the Kuramoto-Sakaguchi Oscillator Network with Asymmetric Order Parameter
• Generating subgraphs in chordal graphs
• Blockchain for Economically Sustainable Wireless Mesh Networks
• Recognizing generating subgraphs revisited
• A Progressively-trained Scale-invariant and Boundary-aware Deep Neural Network for the Automatic 3D Segmentation of Lung Lesions
• Statistical modelling of conidial discharge of entomophthoralean fungi using a newly discovered Pandora species
• Approximation Algorithms for Graph Burning
• Multi-Source Neural Variational Inference
• Deep Learning Framework for Pedestrian Collision Avoidance System (PeCAS)
• Learning with tree-based tensor formats
• A 3-D Projection Model for X-ray Dark-field Imaging
• Time-interval balancing in multi-processor scheduling of composite modular jobs (preliminary description)
• Three-dimensional double helical DNA structure directly revealed from its X-ray fiber diffraction pattern by iterative phase retrieval
• Analysis vs Synthesis – An Investigation of (Co)sparse Signal Models on Graphs
• Bridging Network Embedding and Graph Summarization
• Machine Learning with Abstention for Automated Liver Disease Diagnosis
• On constrained optimization problems solved using CDT
• Simultaneous Ruin Probability for Two-Dimensional Brownian and Lévy Risk Models
• Thompson Sampling for Pursuit-Evasion Problems
• Capital Structure and Speed of Adjustment in U.S. Firms. A Comparative Study in Microeconomic and Macroeconomic Conditions – A Quantille Regression Approach
• Managing App Install Ad Campaigns in RTB: A Q-Learning Approach
• Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
• Semi-supervised Deep Representation Learning for Multi-View Problems
• Multiple Subspace Alignment Improves Domain Adaptation
• Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components
• Product Title Refinement via Multi-Modal Generative Adversarial Learning
• Subsampling to Enhance Efficiency in Input Uncertainty Quantification
• On a Pólya functional for rhombi, isosceles triangles, and thinning convex sets
• SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
• External optimal control of nonlocal PDEs
• Agent Embeddings: A Latent Representation for Pole-Balancing Networks
• Constant payoff in zero-sum stochastic games
• Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity
• The Poisson random effect model for experience ratemaking: limitations and alternative solutions
• Adaptive Hessian Estimation Based Extremum Localization
• Robustness of link prediction under network attacks
• Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition
• M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network
• Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN
• Identification of Internal Faults in Indirect Symmetrical Phase Shift Transformers Using Ensemble Learning
• On the length of the longest consecutive switches
• Variational Community Partition with Novel Network Structure Centrality Prior
• Visual Saliency Maps Can Apply to Facial Expression Recognition
• An efficient ADMM algorithm for high dimensional precision matrix estimation via penalized quadratic loss
• Learning Latent Dynamics for Planning from Pixels
• Tractability of Konig Edge Deletion Problems
• Statistical Inference for Stable Distribution Using EM algorithm
• Time-changed Poisson processes of order $k$
• Vectorization of hypotheses and speech for faster beam search in encoder decoder-based speech recognition
• On the Performance and Convergence of Distributed Stream Processing via Approximate Fault Tolerance
• A differential game on Wasserstein space. Application to weak approachability with partial monitoring
• Forecasting People’s Needs in Hurricane Events from Social Network
• A central limit theorem for descents and major indices in fixed conjugacy classes of $S_n$
• Navigating Assistance System for Quadcopter with Deep Reinforcement Learning
• Holistic Multi-modal Memory Network for Movie Question Answering
• MR-RePair: Grammar Compression based on Maximal Repeats
• The Hidden Shape of Stories Reveals Positivity Bias and Gender Bias
• New Theoretical Bounds and Constructions of Permutation Codes under Block Permutation Metric
• Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
• Learning Personalized End-to-End Goal-Oriented Dialog
• Streaming Hardness of Unique Games
• Matrix Product Operator Restricted Boltzmann Machines
• 140 Gbaud On-Off Keying Links in C-Band for Short-Reach Optical Interconnects
• Subspace Packings
• Different Power Adaption Methods on Fluctuating Two-Ray Fading Channels
• Forming Probably Stable Communities with Limited Interactions
• Depth Image Upsampling based on Guided Filter with Low Gradient Minimization
• Fine-tuning of Language Models with Discriminator
• Importance Weighted Evolution Strategies
• Embedding partial Latin squares in Latin squares with many mutually orthogonal mates
• Newton: A Language for Describing Physics
• Parameterized Synthetic Image Data Set for Fisheye Lens
• Another Note on Intervals in the Hales-Jewett Theorem
• Reciprocal and Positive Real Balanced Truncations for Model Order Reduction of Descriptor Systems
• Angry or Climbing Stairs? Towards Physiological Emotion Recognition in the Wild
• Extending Pretrained Segmentation Networks with Additional Anatomical Structures
• Massive MIMO with a Generalized Channel Model: Fundamental Aspects
• Blind Over-the-Air Computation and Data Fusion via Provable Wirtinger Flow
• Hallucinating very low-resolution and obscured face images
• Global sensitivity analysis for optimization with variable selection
• Combining Learned Lyrical Structures and Vocabulary for Improved Lyric Generation
• Modeling Text Complexity using a Multi-Scale Probit
• Not Just Depressed: Bipolar Disorder Prediction on Reddit
• Surface area deviation between smooth convex bodies and polytopes
• Proprties of biclustering algorithms and a novel biclustering technique based on relative density
• Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
• Design of Low Complexity GFDM Transceiver
• Path integral Monte Carlo method for the quantum anharmonic oscillator
• A Deep Ensemble Framework for Fake News Detection and Classification
• Towards Adversarial Denoising of Radar Micro-Doppler Signatures
• Learning Segmentation Masks with the Independence Prior
• Bias Scheme Reducing Transient Currents and Speeding up Read Operations for 3-D Cross Point PCM
• Joint Probability Distribution of Prediction Errors of ARIMA
• A Generalization of the Matroid Polytope Theorem to Local Forest Greedoids
• Pseudofiniteness in Hrushovski Constructions
• Variational and Optimal Control Approaches for the Second-Order Herglotz Problem on Spheres
• Classifying Patent Applications with Ensemble Methods
• CUNI System for the WMT18 Multimodal Translation Task
• The random walk penalised by its range in dimensions $d\geq 3$
• Weyl-Mahonian Statistics for Weighted Flags of Type A-D
• Analyzing deep CNN-based utterance embeddings for acoustic model adaptation
• Inductively pierced codes and neural toric ideals
• Input Combination Strategies for Multi-Source Transformer Decoder
• End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification
• Optimization of triangular networks with spatial constraints
• On an Annihilation Number Conjecture
• Universal Marginalizer for Amortised Inference and Embedding of Generative Models
• Characterizing $(0,\pm 1)$-matrices with only even-rank principal submatrices in terms of skew-symmetry
• Mutual Information of Wireless Channels and Block-Jacobi Ergodic Operators
• Simple FPGA routing graph compression
• Gaussian Auto-Encoder
• Learning Representations of Missing Data for Predicting Patient Outcomes
• Sliding Window Temporal Graph Coloring
• Deep-learning the Latent Space of Light Transport
• Markov Property in Generative Classifiers
• The Equilibrium States of Large Networks of Erlang Queues
times speedup compared to the initial implementation. Finally, we created a mechanism for automatic hyperparamer optimization using an empirical method.
