nils-herrmann/clean_scirepeval huggingface dataset:
LARGE BATCH SIZE TRAINING OF NEURAL NETWORKS WITH ADVERSARIAL TRAINING AND SECOND-ORDER INFORMATION
Authors: Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael Mahoney

Stochastic Gradient Descent (SGD) methods using randomly selected batches are widely-used to train neural network (NN) models. Performing design exploration to find the best NN for a particular task often requires extensive training with different models on a large dataset,  which is very computationally expensive. The most straightforward method to accelerate this computation is to distribute the batch of SGD over multiple processors. However, large batch training often times leads to degradation in accuracy, poor generalization, and even poor robustness to adversarial attacks.  Existing solutions for large batch training either do not work or require massive hyper-parameter tuning. To address this issue, we propose a novel large batch training method which combines recent results in adversarial training (to regularize against ``sharp minima'') and second order optimization (to use curvature information to change batch size adaptively during training). We extensively evaluate our method on Cifar-10/100, SVHN, TinyImageNet, and ImageNet datasets, using multiple NNs, including residual networks as well as compressed networks such as SqueezeNext.  Our new approach exceeds the performance of the existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $3\times$, respectively). We emphasize that this is achieved without any additional hyper-parameter tuning to tailor our method to any of these experiments.
DEMI: Discriminative Estimator of Mutual Information 
Authors: Ruizhi Liao, Daniel Moyer, Polina Golland, William M Wells

Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data. Recent progress has leveraged neural networks to optimize variational lower bounds on mutual information. Although showing promise for this difficult problem, the variational methods have been theoretically and empirically proven to have serious statistical limitations: 1) many methods struggle to produce accurate estimates when the underlying mutual information is either low or high; 2) the resulting estimators may suffer from high variance. Our approach is based on training a classifier that provides the probability that a data sample pair is drawn from the joint distribution rather than from the product of its marginal distributions. Moreover, we establish a direct connection between mutual information and the average log odds estimate produced by the classifier on a test set, leading to a simple and accurate estimator of mutual information. We show theoretically that our method and other variational approaches are equivalent when they achieve their optimum, while our method sidesteps the variational bound. Empirical results demonstrate high accuracy of our approach and the advantages of our estimator in the context of representation learning.
Generalizing Reinforcement Learning to Unseen Actions
Authors: Ayush Jain*, Andrew Szot*, Jincheng Zhou, Joseph J. Lim

A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances. In this work, we address one such setting which requires solving a task with a novel set of actions. Empowering machines with this ability requires generalization in the way an agent perceives its available actions along with the way it uses these actions to solve tasks. Hence, we propose a framework to enable generalization over both these aspects: understanding an action’s functionality, and using actions to solve tasks through reinforcement learning. Specifically, an agent interprets an action’s behavior using unsupervised representation learning over a collection of data samples reflecting the diverse properties of that action. We employ a reinforcement learning architecture which works over these action representations, and propose regularization metrics essential for enabling generalization in a policy. We illustrate the generalizability of the representation learning method and policy, to enable zero-shot generalization to previously unseen actions on challenging sequential decision-making environments. Our results and videos can be found at
Disentangling Improves VAEs' Robustness to Adversarial Attacks
Authors: Matthew Willetts, Alexander Camuto, Stephen Roberts, Chris Holmes

This paper is concerned with the robustness of VAEs to adversarial attacks. We highlight that conventional VAEs are brittle under attack but that methods recently introduced for disentanglement such as β-TCVAE (Chen et al., 2018) improve robustness, as demonstrated through a variety of previously proposed adversarial attacks (Tabacof et al. (2016); Gondim-Ribeiro et al. (2018); Kos et al.(2018)). This motivated us to develop Seatbelt-VAE, a new hierarchical disentangled VAE that is designed to be significantly more robust to adversarial attacks than existing approaches, while retaining high quality reconstructions.
Cover Filtration and Stable Paths in the Mapper
Authors: Dustin L. Arendt, Matthew Broussard, Bala Krishnamoorthy, Nathaniel Saul

The contributions of this paper are two-fold. We define a new filtration called the cover filtration built from a single cover based on a generalized Steinhaus distance, which is a generalization of Jaccard distance. We then develop a language and theory for stable paths within this filtration, inspired by ideas of persistent homology. This framework can be used to develop several new learning representations in applications where an obvious metric may not be defined but a cover is readily available. We demonstrate the utility of our framework as applied to recommendation systems and explainable machine learning.

We demonstrate a new perspective for modeling recommendation system data sets that does not require manufacturing a bespoke metric. As a direct application, we find that the stable paths identified by our framework in a movies data set represent a sequence of movies constituting a gentle transition and ordering from one genre to another.

For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations. Our framework provides an alternative way of building a filtration from a single mapper that is then used to explore stable paths. As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set. We show that the stable paths in the cover filtration provide improved explanations of relationships between subpopulations of images.
On Dynamic Noise Influence in Differential Private Learning
Authors: Junyuan Hong, Zhangyang Wang, Jiayu Zhou

Protecting privacy in learning while maintaining the model performance has become increasingly critical in many applications that involve sensitive data. Private Gradient Descent (PGD) is a commonly used private learning framework, which adds noise according to the Differential Privacy protocol.Recent studies show that dynamic privacy schedules of decreasing noise magnitudes can improve loss at the final iteration, and yet theoretical understandings of the effectiveness of such schedules and their connections to optimization algorithms remain limited. In this paper, we provide comprehensive analysis of noise influence in dynamic privacy schedules to answer these critical questions. We first present a dynamic noise schedule minimizing the utility upper bound of PGD, and show how the noise influence from each optimization step collectively impacts utility of the final model. Our study also reveals how impacts from dynamic noise influence change when momentum is used. We empirically show the connection exists for general non-convex losses, and the influence is greatly impacted by the loss curvature.
FTSO: Effective NAS via First Topology Second Operator
Authors: Likang Wang, Lei Chen

Existing one-shot neural architecture search (NAS) methods generally contain a giant supernet, which leads to heavy computational cost. Our method, named FTSO, separates the whole architecture search into two sub-steps. In the first step, we only search for the topology, and in the second step, we only search for the operators. FTSO not only reduces NAS’s search time from days to 0.68 seconds, but also significantly improves the accuracy. Specifically, our experiments on ImageNet show that within merely 18 seconds, FTSO can achieve 76.4% testing accuracy, 1.5% higher than the baseline, PC-DARTS. In addition, FTSO can reach 97.77% testing accuracy, 0.27% higher than the baseline, with 99.8% of search time saved on CIFAR10.
On the Margin Theory of Feedforward Neural Networks
Authors: Colin Wei, Jason Lee, Qiang Liu, Tengyu Ma

Past works have shown that, somewhat surprisingly, over-parametrization can help generalization in neural networks. Towards explaining this phenomenon, we adopt a margin-based perspective.  for multi-layer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for deep networks. In the case of two-layer networks, an infinite-width neural network enjoys the best generalization guarantees. The typical infinite feature methods are kernel methods; we compare the neural net margin with that of kernel methods and construct natural instances where kernel methods have much weaker generalization guarantees. We validate this gap between the two approaches empirically. Finally, this infinite-neuron viewpoint is also fruitful for analyzing optimization. We show that a perturbed gradient flow on infinite-size networks finds a global optimizer in polynomial time.
Video Forgery Detection Using Multiple Cues on Fusion of EfficientNet and Swin Transformer
Authors: Chenyu Liu, Jia Li, Junxian Duan, Huaibo Huang

The rapid development of video processing technology makes it easy for people to forge videos without leaving visual artifacts. The spread of forged videos may lead to moral and legal consequences and pose a potential threat to people's lives and social stability. So it is significant to identify deepfake video information. Although the previous detection methods have achieved high accuracy, the generalization is poor when facing unprecedented data in the real scene. There are three fundamental reasons. The first is that capturing the general clue of artifacts is difficult. The second is that selecting the appropriate model is challenging in specific feature extraction. The third is that exploiting fully and effectively the extracted features is hard. We find that the high-frequency information in the image and the texture in the shallow layer of the model expose the subtle artifacts. The optical flow of the real video has variations while the optical flow of the deepfake video has rarely variations. Furthermore, consecutive frames in the real video have temporal consistency. In this paper, we propose a dual-branch video forgery detection model named ENST, which integrates parallelly and interactively EfficientNet-B5 and Swin Transformer. Specifically, EfficientNet-B5 extracts the artifacts information of high frequency and texture in the shallow layer of the model. Swin Transformer captures the subtle discrepancies between optical flows. To extract more robust face features, we design a new loss function for EfficientNet-B5. In addition, we also introduce the attention mechanism into EfficientNet-B5 to enhance the extracted features. We conduct test experiments on FaceForensics++ and Celeb-DF (v2) datasets, and comprehensive results show that ENST has higher accuracy and generalization, which is superior to the most advanced methods.
Kronecker Attention Networks
Authors: Hongyang Gao, Zhengyang Wang, Shuiwang Ji

Attention operators have been applied on both 1-D data like texts and higher-order data such as images and videos. Use of attention operators on high-order data requires flattening of the spatial or spatial-temporal dimensions into a vector, which is assumed to follow a multivariate normal distribution. This not only incurs excessive requirements on computational resources, but also fails to preserve structures in data. In this work, we propose to avoid flattening by developing Kronecker attention operators (KAOs) that operate on high-order tensor data directly. KAOs lead to dramatic reductions in computational resources. Moreover, we analyze KAOs theoretically from a probabilistic perspective and point out that KAOs assume the data follow matrix-variate normal distributions. Experimental results show that KAOs reduce the amount of required computational resources by a factor of hundreds, with larger factors for higher-dimensional and higher-order data. Results also show that networks with KAOs outperform models without attention, while achieving competitive performance as those with original attention operators.
When Does Self-supervision Improve Few-shot Learning?
Authors: Jong-Chyi Su, Subhransu Maji, Bharath Hariharan

We present a technique to improve the generalization of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. Although recent research has shown benefits of self-supervised learning (SSL) on large unlabeled datasets, its utility on small datasets is unknown. We find that SSL reduces the relative error rate of few-shot meta-learners by 4%-27%, even when the datasets are small and only utilizing images within the datasets. The improvements are greater when the training set is smaller or the task is more challenging. Though the benefits of SSL may increase with larger training sets, we observe that SSL can have a negative impact on performance when there is a domain shift between distribution of images used for meta-learning and SSL. Based on this analysis we present a technique that automatically select images for SSL from a large, generic pool of unlabeled images for a given dataset using a domain classifier that provides further improvements. We present results using several meta-learners and self-supervised tasks across datasets with varying degrees of domain shifts and label sizes to characterize the effectiveness of SSL for few-shot learning.
Two steps at a time --- taking GAN training in stride with Tseng's method
Authors: Axel Böhm, Michael Sedlmayer, Ernö Robert Csetnek, Radu Ioan Bot

Motivated by the training of Generative Adversarial Networks (GANs), we study methods for solving minimax problems with additional nonsmooth regularizers.
We do so by employing \emph{monotone operator} theory, in particular the \emph{Forward-Backward-Forward (FBF)} method, which avoids the known issue of limit cycling by correcting each update by a second gradient evaluation.
Furthermore, we propose a seemingly new scheme which recycles old gradients to mitigate the additional computational cost.
In doing so we rediscover a known method, related to \emph{Optimistic Gradient Descent Ascent (OGDA)}.
For both schemes we prove novel convergence rates for convex-concave minimax problems via a unifying approach. The derived error bounds are in terms of the gap function for the ergodic iterates.
For the deterministic and the stochastic problem we show a convergence rate of $\mathcal{O}(\nicefrac{1}{k})$ and $\mathcal{O}(\nicefrac{1}{\sqrt{k}})$, respectively.
We complement our theoretical results with empirical improvements in the training of Wasserstein GANs on the CIFAR10 dataset.
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining
Authors: Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, Somesh Jha

Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle in the open world, facing various types of adversarial OOD inputs. While methods leveraging auxiliary OOD data have emerged, our analysis reveals a key insight that the majority of auxiliary OOD examples may not meaningfully improve the decision boundary of the OOD detector. In this paper, we provide a theoretically motivated method, Adversarial Training with informative Outlier Mining (ATOM), which improves the robustness of OOD detection. We show that, by mining informative auxiliary OOD data, one can significantly improve OOD detection performance, and somewhat surprisingly, generalize to unseen adversarial attacks. ATOM achieves state-of-the-art performance under a broad family of classic and adversarial OOD evaluation tasks. For example, on the CIFAR-10 in-distribution dataset, ATOM reduces the FPR95 by up to 57.99\% under adversarial OOD inputs, surpassing the previous best baseline by a large margin.
Why Should I Trust You, Bellman? Evaluating the Bellman Objective with Off-Policy Data
Authors: Scott Fujimoto, David Meger, Doina Precup, Ofir Nachum, Shixiang Shane Gu

In this work, we analyze the effectiveness of the Bellman equation as a proxy objective for value prediction accuracy in off-policy evaluation. While the Bellman equation is uniquely solved by the true value function over all state-action pairs, we show that in the finite data regime, the Bellman equation can be satisfied exactly by infinitely many suboptimal solutions. This eliminates any guarantees relating Bellman error to the accuracy of the value function. We find this observation extends to practical settings; when computed over an off-policy dataset, the Bellman error bears little relationship to the accuracy of the value function. Consequently, we show that the Bellman error is a poor metric for comparing value functions, and therefore, an ineffective objective for off-policy evaluation. Finally, we discuss differences between Bellman error and the non-stationary objective used by iterative methods and deep reinforcement learning, and highlight how the effectiveness of this objective relies on generalization during training.
A Painless Attention Mechanism for Convolutional Neural Networks
Authors: Pau Rodríguez, Guillem Cucurull, Jordi Gonzàlez, Josep M. Gonfaus, Xavier Roca

We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. The proposed mechanism reuses CNN feature activations to find the most informative parts of the image at different depths with the help of gating mechanisms and without part annotations. Thus, it can be used to augment any layer of a CNN to extract low- and high-level local information to be more discriminative. 

Differently, from other approaches, the mechanism we propose just needs a single pass through the input and it can be trained end-to-end through SGD. As a consequence, the proposed mechanism is modular, architecture-independent, easy to implement, and faster than iterative approaches.

Experiments show that, when augmented with our approach, Wide Residual Networks systematically achieve superior performance on each of five different fine-grained recognition datasets: the Adience age and gender recognition benchmark, Caltech-UCSD Birds-200-2011, Stanford Dogs, Stanford Cars, and UEC Food-100, obtaining competitive and state-of-the-art scores.
Denoising Diffusion Gamma Models
Authors: Eliya Nachmani, Robin San Roman, Lior Wolf

Generative diffusion processes are an emerging and effective tool for image and speech generation. In the existing methods, the underlying noise distribution of the diffusion process is Gaussian noise. However, fitting distributions with more degrees of freedom could improve the performance of such generative models. In this work, we investigate other types of noise distribution for the diffusion process. Specifically, we introduce the Denoising Diffusion Gamma Model (DDGM) and show that noise from Gamma distribution provides improved results for image and speech generation. Our approach preserves the ability to efficiently sample state in the training diffusion process while using Gamma noise.
Feature prioritization and regularization improve standard accuracy and adversarial robustness
Authors: Chihuang Liu, Joseph JaJa

Adversarial training has been successfully applied to build robust models at a certain cost. While the robustness of a model increases, the standard classification accuracy declines. This phenomenon is suggested to be an inherent trade-off. We propose a model that employs feature prioritization by a nonlinear attention module and $L_2$ feature regularization to improve the adversarial robustness and the standard accuracy relative to adversarial training. The attention module encourages the model to rely heavily on robust features by assigning larger weights to them while suppressing non-robust features. The regularizer encourages the model to extracts similar features for the natural and adversarial images, effectively ignoring the added perturbation. In addition to evaluating the robustness of our model, we provide justification for the attention module and propose a novel experimental strategy that quantitatively demonstrates that our model is almost ideally aligned with salient data characteristics. Additional experimental results illustrate the power of our model relative to the state of the art methods.
Scalable Graph Neural Networks for Heterogeneous Graphs
Authors: Lingfan Yu, Jiajun Shen, Jinyang Li, Adam Lerer

Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks by simply operating on graph-smoothed node features, rather than using end-to-end learned feature hierarchies that are challenging to scale to large graphs. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities. We propose Neighbor Averaging over Relation Subgraphs (NARS), which trains a classifier on neighbor-averaged features for randomly-sampled subgraphs of the ‘metagraph‘ of relations. We describe optimizations to allow these sets of node features to be computed in a memory-efficient way, both at training and inference time. NARS achieves a new state of the art accuracy on several benchmark datasets, outperforming more expensive GNN-based methods.
A Way out of the Odyssey: Analyzing and Combining Recent Insights for LSTMs
Authors: Shayne Longpre, Sabeek Pradhan, Caiming Xiong, Richard Socher

LSTMs have become a basic building block for many deep NLP models. In recent years, many improvements and variations have been proposed for deep sequence models in general, and LSTMs in particular. We propose and analyze a series of architectural modifications for LSTM networks resulting in improved performance for text classification datasets. We observe compounding improvements on traditional LSTMs using Monte Carlo test-time model averaging, deep vector averaging (DVA), and residual connections, along with four other suggested modifications. Our analysis provides a simple, reliable, and high quality baseline model.