nils-herrmann/clean_scirepeval huggingface dataset:
Small steps and giant leaps: Minimal Newton solvers for Deep Learning
Authors: Joao Henriques, Sebastien Ehrhardt, Samuel Albanie, Andrea Vedaldi

We propose a fast second-order method that can be used as a drop-in replacement for current deep learning solvers. Compared to stochastic gradient descent (SGD), it only requires two additional forward-mode automatic differentiation operations per iteration, which has a computational cost comparable to two standard forward passes and is easy to implement. Our method addresses long-standing issues with current second-order solvers, which invert an approximate Hessian matrix every iteration exactly or by conjugate-gradient methods, procedures that are much slower than a SGD step. Instead, we propose to keep a single estimate of the gradient projected by the inverse Hessian matrix, and update it once per iteration with just two passes over the network. This estimate has the same size and is similar to the momentum variable that is commonly used in SGD. No estimate of the Hessian is maintained.
We first validate our method, called CurveBall, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle. We then train several large models on CIFAR and ImageNet, including ResNet and VGG-f networks, where we demonstrate faster convergence with no hyperparameter tuning. We also show our optimiser's generality by testing on a large set of randomly-generated architectures.
On the Evolution of Neuron Communities in a Deep Learning Architecture
Authors: Sakib Mostafa, Debajyoti Mondal

Deep learning techniques are increasingly being adopted for classification tasks over the past decade, yet explaining how deep learning architectures can achieve state-of-the-art performance is still an elusive goal. While all the training information is embedded deeply in a trained model, we still do not understand much about its performance by only analyzing the model. This paper examines the neuron activation patterns of deep learning-based classification models and explores whether the models' performances can be explained through neurons' activation behavior. We propose two approaches: one that models neurons' activation behavior as a graph and examines whether the neurons form meaningful communities, and the other examines the predictability of neurons' behavior using entropy. Our comprehensive experimental study reveals that both  the community quality and entropy can provide new insights into the deep learning models' performances, thus paves a novel way of explaining deep learning models directly from the neurons' activation pattern
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Unsupervised Video-to-Video Translation
Authors: Dina Bashkirova, Ben Usman, Kate Saenko

Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time. In this paper, we formulate a new task of unsupervised video-to-video translation, which poses its own unique challenges. Translating video implies learning not only the appearance of objects and scenes but also realistic motion and transitions between consecutive frames. We investigate the performance of per-frame video-to-video translation using existing image-to-image translation networks, and propose a spatio-temporal 3D translator as an alternative solution to this problem. We evaluate our 3D method on multiple synthetic datasets, such as moving colorized digits, as well as the realistic segmentation-to-video GTA dataset and a new CT-to-MRI volumetric images translation dataset. Our results show that frame-wise translation produces realistic results on a single frame level but underperforms significantly on the scale of the whole video compared to our three-dimensional translation approach, which is better able to learn the complex structure of video and motion and continuity of object appearance.
Generalization and Regularization in DQN
Authors: Jesse Farebrother, Marlos C. Machado, Michael Bowling

Deep reinforcement learning (RL) algorithms have shown an impressive ability to learn complex control policies in high-dimensional environments. However, despite the ever-increasing performance on popular benchmarks like the Arcade Learning Environment (ALE), policies learned by deep RL algorithms can struggle to generalize when evaluated in remarkably similar environments. These results are unexpected given the fact that, in supervised learning, deep neural networks often learn robust features that generalize across tasks. In this paper, we study the generalization capabilities of DQN in order to aid in understanding this mismatch between generalization in deep RL and supervised learning methods. We provide evidence suggesting that DQN overspecializes to the domain it is trained on. We then comprehensively evaluate the impact of traditional methods of regularization from supervised learning, $\ell_2$ and dropout, and of reusing learned representations to improve the generalization capabilities of DQN. We perform this study using different game modes of Atari 2600 games, a recently introduced modification for the ALE which supports slight variations of the Atari 2600 games used for benchmarking in the field. Despite regularization being largely underutilized in deep RL, we show that it can, in fact, help DQN learn more general features. These features can then be reused and fine-tuned on similar tasks, considerably improving the sample efficiency of DQN.
Adversarially Learned Mixture Model
Authors: Andrew Jesson, Cécile Low-Kam, Tanya Nair, Florian Soudan, Florent Chandelier, Nicolas Chapados

The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering. The AMM is the first adversarially optimized method to model the conditional dependence between inferred continuous and categorical latent variables. Experiments on the MNIST and SVHN datasets show that the AMM allows for semantic separation of complex data when little or no labeled data is available. The AMM achieves unsupervised clustering error rates of 3.32% and 20.4% on the MNIST and SVHN datasets, respectively.  A semi-supervised extension of the AMM achieves a classification error rate of 5.60% on the SVHN dataset.
LEARNING DIFFICULT PERCEPTUAL TASKS WITH HODGKIN-HUXLEY NETWORKS
Authors: Alan Lockett, Ankit Patel, Paul Pfaffinger

This paper demonstrates that a computational neural network model using ion channel-based conductances to transmit information can solve standard computer vision datasets at near state-of-the-art performance. Although not fully biologically accurate, this model incorporates fundamental biophysical principles underlying the control of membrane potential and the processing of information by Ohmic ion channels. The key computational step employs Conductance-Weighted Averaging (CWA) in place of the traditional affine transformation, representing a fundamentally different computational principle. 
Importantly, CWA based networks are self-normalizing and range-limited. We also demonstrate for the first time that a network with excitatory and inhibitory neurons and nonnegative synapse strengths can successfully solve computer vision problems. Although CWA models do not yet surpass the current state-of-the-art in deep learning, the results are competitive on CIFAR-10. There remain avenues for improving these networks, e.g. by more closely modeling ion channel function and connectivity patterns of excitatory and inhibitory neurons found in the brain.
Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks
Authors: Yihan Wu, Aleksandar Bojchevski, Heng Huang

There is growing theoretical and empirical evidence that flatter local minima tend to improve generalization. An efficient and effective technique for finding such minima is Adversarial Weight Perturbation (AWP). The main idea is to minimize the loss w.r.t. a bounded worst-case perturbation of the model parameters by (approximately) solving an associated min-max problem. Intuitively, we favor local minima with a small loss in a neighborhood around them. The benefits of AWP, and more generally the connections between flatness and generalization, have been extensively studied for i.i.d. data such as images. In this paper we initiate the first study of this phenomenon for graph data. Along the way, we identify a vanishing-gradient issue with all existing formulations of AWP and we propose Weighted Truncated AWP (WT-AWP) to alleviate this issue. We show that regularizing graph neural networks with WT-AWP consistently improves both natural and robust generalization across many different graph learning tasks and models.
Parallel Neural Text-to-Speech
Authors: Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao

In this work, we first propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains 46.7 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. ParaNet also produces stable alignment between text and speech on the challenging test sentences by iteratively improving the attention in a layer-by-layer manner. Based on ParaNet, we build the first fully parallel neural text-to-speech system using parallel neural vocoders, which can synthesize speech from text through a single feed-forward pass.  We investigate several parallel vocoders within the TTS system, including variants of IAF vocoders and bipartite flow vocoder.
AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering
Authors: Xingyu Xie, Minjuan Zhu, Yan Wang, Lei Zhang

Unsupervised representation learning is essential in the field of machine learning, and accurate neighbor clusters of representation show great potential to support unsupervised image classification. This paper proposes a VAE (Variational Autoencoder) based network and a clustering method to achieve adaptive neighbor clustering to support the self-supervised classification. The proposed network encodes the image into the representation with boundary information, and the proposed cluster method takes advantage of the boundary information to deliver adaptive neighbor cluster results.  Experimental evaluations show that the proposed method outperforms state-of-the-art representation learning methods in terms of neighbor clustering accuracy. Particularly, AC-VAE achieves 95\% and 82\% accuracy on CIFAR10 dataset when the average neighbor cluster sizes are 10 and 100. Furthermore, the neighbor cluster results are found converge within the clustering range ($\alpha\leq2$), and the converged neighbor clusters are used to support the self-supervised classification. The proposed method delivers classification results that are competitive with the state-of-the-art and reduces the super parameter $k$ in KNN (K-nearest neighbor), which is often used in self-supervised classification.
Extract Local Inference Chains of Deep Neural Nets
Authors: Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang

We study how to explain the main steps/chains of inference that a deep neural net (DNN) relies on to produce predictions in a local region of data space. This problem is related to network pruning and interpretable machine learning but the highlighted differences are: (1) fine-tuning of neurons/filters is forbidden: only exact copies are allowed; (2) we target an extremely high pruning rate, e.g., $\geq 95\%$; (3) the interpretation is for the whole inference process in a local region rather than for individual neurons/filters or on a single sample. In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region. Thereby, \name~can extract an extremely small sub-network composed of filters exactly copied from the original DNN by removing the filters/layers with small scores. We then visualize the sub-network by applying existing interpretation technique to the retained layer/filter/neurons and on any sample from the local region. Its architecture reveals how the inference process stitches and integrates the information layer by layer and filter by filter. We provide detailed and insightful case studies together with three quantitative analyses over thousands of trials to demonstrate the quality, sparsity, fidelity and accuracy of the interpretation within the assigned local regions and over unseen data. In our empirical study, \name~significantly enriches the interpretation and makes the inner mechanism of DNNs more transparent than before.
Invariance vs Robustness of Neural Networks
Authors: Sandesh Kamath, Amit Deshpande, K V Subrahmanyam

Neural networks achieve human-level accuracy on many standard datasets used in image classification. The next step is to achieve better generalization to natural (or non-adversarial) perturbations as well as known pixel-wise adversarial perturbations of inputs. Previous work has studied generalization to natural geometric transformations (e.g., rotations) as invariance, and generalization to adversarial perturbations as robustness. In this paper, we examine the interplay between invariance and robustness. We empirically study the following two cases:(a) change in adversarial robustness as we improve only the invariance using equivariant models and training augmentation, (b) change in invariance as we improve only the adversarial robustness using adversarial training. We observe that the rotation invariance of equivariant models (StdCNNs and GCNNs) improves by training augmentation with progressively larger rotations but while doing so, their adversarial robustness does not improve, or worse, it can even drop significantly on datasets such as MNIST. As a plausible explanation for this phenomenon we observe that the average perturbation distance of the test points to the decision boundary decreases as the model learns larger and larger rotations. On the other hand, we take adversarially trained LeNet and ResNet models which have good \ell_\infty adversarial robustness on MNIST and CIFAR-10, and observe that adversarially training them with progressively larger norms keeps their rotation invariance essentially unchanged. In fact, the difference between test accuracy on unrotated test data and on randomly rotated test data upto \theta , for all \theta in [0, 180], remains essentially unchanged after adversarial training . As a plausible explanation for the observed phenomenon we show empirically that the principal components of adversarial perturbations and perturbations given by small rotations are nearly orthogonal
Normalizing Flows for Calibration and Recalibration
Authors: Achintya Gopal, Aaron Key

In machine learning, due to model misspecification and overfitting, estimates of the aleatoric uncertainty are often inaccurate.
One approach to fix this is isotonic regression, in which a monotonic function is fit on a validation set to map the model's CDF to an optimally calibrated CDF. However, this makes it infeasible to compute additional statistics of interest on the model distribution (such as the mean). In this paper, through a reframing of recalibration as MLE, we replace isotonic regression with normalizing flows. This allows us to retain the ability to compute the statistical properties of the model (such as closed-form likelihoods, mean, correlation, etc.) and provides an opportunity for additional capacity at the cost of possible overfitting.  Most importantly, the fundamental properties of normalizing flows allow us to generalize recalibration to conditional and multivariate distributions. To aid in detecting miscalibration and measuring our success at fixing it, we use a simple extension of the calibration Q-Q plot.
A dynamic game approach to training robust deep policies
Authors: Olalekan Ogunmolu

We present a method for evaluating the sensitivity of deep reinforcement learning (RL) policies. We also formulate a zero-sum dynamic game for designing robust deep reinforcement learning policies. Our approach mitigates the brittleness of policies when agents are trained in a simulated environment and are later exposed to the real world where it is hazardous to employ RL policies. This framework for training deep RL policies involve a zero-sum  dynamic game against an adversarial agent, where the goal is to drive the system dynamics to a saddle region. Using a variant of the guided policy search algorithm, our agent learns to adopt robust policies that require less samples for learning the dynamics and performs better than the GPS algorithm. Without loss of generality, we demonstrate that deep RL policies trained in this fashion will be maximally robust to a ``worst" possible adversarial disturbances.
Continual Learning Using Task Conditional Neural Networks
Authors: Honglin Li, Frieder Ganz, David J. Sharp, Payam M. Barnaghi

Conventional deep learning models have limited capacity in learning multiple tasks sequentially. The issue of forgetting the previously learned tasks in continual learning is known as catastrophic forgetting or interference. When the input data or the goal of learning changes, a continual model will learn and adapt to the new status. However, the model will not remember or recognise any revisits to the previous states. This causes performance reduction and re-training curves in dealing with periodic or irregularly reoccurring changes in the data or goals. Dynamic approaches, which assign new neuron resources to the upcoming tasks, are introduced to address this issue. However, most of the dynamic methods need task information about the upcoming tasks during the inference phase to activate the corresponding neurons. To address this issue, we introduce Task Conditional Neural Network which allows the model to identify the task information automatically. The proposed model can continually learn and embed new tasks into the model without losing the information about previously learned tasks. We evaluate the proposed model combined with the mixture of experts approach on the MNIST and CIFAR100 datasets and show how it significantly improves the continual learning process without requiring task information in advance.
Switching-Aligned-Words Data Augmentation for Neural Machine Translation
Authors: Fengshun Xiao, Zuchao Li, hai zhao

In neural machine translation (NMT), data augmentation methods such as back-translation make it possible to use extra monolingual data to help improve translation performance, while it needs extra training data and the in-domain monolingual data is not always available. In this paper, we present a novel data augmentation method for neural machine translation by using only the original training data without extra data. More accurately, we randomly replace words or mixup with their aligned alternatives in another language when training neural machine translation models. Since aligned word pairs appear in the same position of each other during training, it is helpful to form bilingual embeddings which are proved useful to provide a performance boost \citep{liu2019shared}. Experiments on both small and large scale datasets show that our method significantly outperforms the baseline models.
Generalisation Guarantees For Continual Learning With Orthogonal Gradient Descent
Authors: Mehdi Abbana Bennani, Thang Doan, Masashi Sugiyama

In Continual Learning settings, deep neural networks are prone to Catastrophic Forgetting. Orthogonal Gradient Descent (Farajtabar et al., 2019) was proposed to tackle the challenge. However, no theoretical guarantees have been proven yet. We present a theoretical framework to study Continual Learning algorithms in the NTK regime. This framework comprises closed form expression of the model through tasks and proxies for transfer learning, generalisation and tasks similarity. In this framework, we prove that OGD is robust to Catastrophic Forgetting then derive the first generalisation bound for SGD and OGD for Continual Learning. Finally, we study the limits of this framework in practice for OGD and highlight the importance of the NTK variation for Continual Learning.
Improving Model Robustness with Latent Distribution Locally and Globally
Authors: Zhuang QIAN, Shufei Zhang, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi

We propose a novel adversarial training method which leverages both the local and global information to defend  adversarial attacks. Existing adversarial training methods usually generate adversarial perturbations  locally in a supervised manner and fail to consider the data manifold information in a global way. Consequently, the resulting  adversarial examples may corrupt the underlying data structure and are typically biased towards the decision boundary. In this work, we exploit both the local and global information of data manifold to generate adversarial examples in an unsupervised manner. Specifically, we design our novel framework  via an adversarial game between  a discriminator and a classifier: the discriminator is  learned to differentiate the latent distributions  of the natural data and the perturbed counterpart, while the classifier is trained to recognize accurately the perturbed examples as well as enforcing the invariance between the two latent distributions. We conduct a series of analysis on the model robustness and also verify the effectiveness of our proposed method empirically. Experimental results show that our method  substantially outperforms the recent state-of-the-art (i.e. Feature Scattering) in defending adversarial attacks  by a large accuracy margin  (e.g. $17.0\%$ and $18.1\%$ on SVHN dataset, $9.3\%$ and $17.4\%$ on CIFAR-10 dataset, $6.0\%$ and $16.2\%$ on CIFAR-100 dataset for defending PGD20 and CW20 attacks respectively).
Noisy Information Bottlenecks for Generalization
Authors: Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber

We propose Noisy Information Bottlenecks (NIB) to limit mutual information between learned parameters and the data through noise. We show why this benefits generalization and allows mitigation of model overfitting both for supervised and unsupervised learning, even for arbitrarily complex architectures. We reinterpret methods including the Variational Autoencoder, beta-VAE, network weight uncertainty and a variant of dropout combined with weight decay as special cases of our approach, explaining and quantifying regularizing properties and vulnerabilities within information theory.
EXPLORATION OF EFFICIENT ON-DEVICE ACOUSTIC MODELING WITH NEURAL NETWORKS
Authors: Wonyong Sung, Lukas Lee, Jinwhan Park

Real-time speech recognition on mobile and embedded devices is an important application of neural networks. Acoustic modeling is the fundamental part of speech recognition and is usually implemented with long short-term memory (LSTM)-based recurrent neural networks (RNNs). However, the single thread execution of an LSTM RNN is extremely slow in most embedded devices because the algorithm needs to fetch a large number of parameters from the DRAM for computing each output sample. We explore a few acoustic modeling algorithms that can be executed very efficiently on embedded devices. These algorithms reduce the overhead of memory accesses using multi-timestep parallelization that computes multiple output samples at a time by reading the parameters only once from the DRAM. The algorithms considered are the quasi RNNs (QRNNs), Gated ConvNets, and diagonalized LSTMs. In addition, we explore neural networks that equip one-dimensional (1-D) convolution at each layer of these algorithms, and by which can obtain a very large performance increase in the QRNNs and Gated ConvNets. The experiments were conducted using two tasks, one is the connectionist temporal classification (CTC)-based end-to-end speech recognition on WSJ corpus and the other is the phoneme classification on TIMIT dataset. We not only significantly increase the execution speed but also obtain a much higher accuracy, compared to LSTM RNN-based modeling. Thus, this work can be applicable not only to embedded system-based implementations but also to server-based ones.