AutoPruner
Channel pruning is an important family of methods to speedup deep model’s inference. Previous filter pruning algorithms regard channel pruning and model fine-tuning as two independent steps. This paper argues that combining them in a single end-to-end trainable system will lead to better results. We propose an efficient channel selection layer, namely AutoPruner, to find less important filters automatically in a joint training manner. AutoPruner takes previous activation responses as input and generates a true binary index code for pruning. Hence, all the filters corresponding to zero index values can be removed safely after training. We empirically demonstrate that the gradient information of this channel selection layer is also helpful for the whole model training. Compared with previous state-of-the-art pruning algorithms, AutoPruner achieves significantly better performance. Furthermore, ablation experiments show that the proposed novel mini-batch pooling and binarization operations are vital for the success of filter pruning. …

Skilled Experience Catalogue (SEC)
In this paper, we introduce a skill-balancing mechanism for adversarial non-player characters (NPCs), called Skilled Experience Catalogue (SEC). The objective of this mechanism is to approximately match the skill level of an NPC to an opponent in real-time. We test the technique in the context of a First-Person Shooter (FPS) game. Specifically, the technique adjusts a reinforcement learning NPC’s proficiency with a weapon based on its current performance against an opponent. Firstly, a catalogue of experience, in the form of stored learning policies, is built up by playing a series of training games. Once the NPC has been sufficiently trained, the catalogue acts as a timeline of experience with incremental knowledge milestones in the form of stored learning policies. If the NPC is performing poorly, it can jump to a later stage in the learning timeline to be equipped with more informed decision-making. Likewise, if it is performing significantly better than the opponent, it will jump to an earlier stage. The NPC continues to learn in real-time using reinforcement learning but its policy is adjusted, as required, by loading the most suitable milestones for the current circumstances. …

Feature Crossing
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses. …

Learned Global Ranking (LeGR)
Filter pruning has shown to be effective for learning resource-constrained convolutional neural networks (CNNs). However, prior methods for resource-constrained filter pruning have some limitations that hinder their effectiveness and efficiency. When searching for constraint-satisfying CNNs, prior methods either alter the optimization objective or adopt local search algorithms with heuristic parameterization, which are sub-optimal, especially in low-resource regime. From the efficiency perspective, prior methods are often costly to search for constraint-satisfying CNNs. In this work, we propose learned global ranking, dubbed LeGR, which improves upon prior art in the two aforementioned dimensions. Inspired by theoretical analysis, LeGR is parameterized to learn layer-wise affine transformations over the filter norms to construct a learned global ranking. With global ranking, resource-constrained filter pruning at various constraint levels can be done efficiently. We conduct extensive empirical analyses to demonstrate the effectiveness of the proposed algorithm with ResNet and MobileNetV2 networks on CIFAR-10, CIFAR-100, Bird-200, and ImageNet datasets. Code is publicly available at https://…/LeGR. …