For ease and reliable comparison, we report the average of all Rank-1 and mAP results on all test datasets over several random runs for ablation study and parameter analysis. This is denoted by mAcc. There are three reasons that we use mAcc. It is a unified measure, which is convenient for algorithm comparison. Both Rank-1 and mAP are accuracy measures ranging from 0%-100%, thus averaging them is possible. Besides, if a method's mAcc is 1% higher than another method, on average it means that every single measure on each dataset has been increased by 1%, which is a perceptible achievement.

Prior works on Post-training Quantization (PTQ) typically separate a neural network into sub-nets and quantize them sequentially. This process pays little attention to the dependency across the sub-nets, hence is less optimal. In this paper, we propose a novel Network-Wise Quantization (NWQ) approach to fully leveraging inter-layer dependency. NWQ faces a larger scale combinatorial optimization problem of discrete variables than in previous works, which raises two major challenges: over-fitting and discrete optimization problem. NWQ alleviates over fitting via a Activation Regularization (AR) technique, which better controls the activation distribution. To optimize discrete variables, NWQ introduces Annealing Softmax (ASoftmax) and Annealing Mixup (AMixup) to progressively transition quantized weights and activations from continuity to discretization, respectively. Extensive experiments demonstrates that NWQ outperforms prior state-of-the-art approaches by a large margin: 20.24% for the challenging configuration of MobileNetV2 with 2 bits on ImageNet, pushing extremely low-bit PTQ from feasibility to usability. In addition, NWQ is able to achieve competitive or better results with only 10% computation cost of previous works.

An increasing need of running Convolutional Neural Network (CNN) models on mobile devices with limited computing power and memory resource encourages studies on efficient model design. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and MobileNetV2. However, all these models are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.

Homomorphic encryption (HE)-based deep neural network (DNN) inference protects data and model privacy but suffers from significant computation overhead. We observe transforming the DNN weights into circulant matrices converts general matrix-vector multiplications into HE-friendly 1-dimensional convolutions, drastically reducing the HE computation cost.

Figure 2: Anexampleofadynamic (left) andstatic (right) neuralgraph. Claim: Assume L is Lipschitzcontinuous.

Neural Information Processing Systems http://nips.cc/

Our gains are indeed large. EvoNorm-S0 is the state-of-the-art in the small batch size regime (Table 4), outperforming BN-ReLU by 7.8% We achieve clear gains over other influential works such as GroupNorm (GN). We'd also like to emphasize that EvoNorms beat BN-ReLU on 12 (out of 14) different classification models/training These are significant considering the predominance of BN-ReLU in ML models. R3: "the overall search algorithm lacks some novelty." "yet another AutoML paper" (with the expectation that some fancy search algorithms must be proposed), but rather under R2, R4: Can EvoNorms generalize to deeper variants (e.g., ResNet-101) and architecture families not included MnasNet, EfficientNet-B5, Mask R-CNN + FPN/SpineNet and BigGAN-none of them was used during search.