nils-herrmann/clean_scirepeval huggingface dataset:
On Symmetry and Initialization for Neural Networks
Authors: Ido Nachum, Amir Yehudayoff

This work provides an additional step in the theoretical understanding of neural networks. We consider neural networks with one hidden layer and show that when learning symmetric functions, one can choose initial conditions so that standard SGD training efficiently produces generalization guarantees. We empirically verify this and show that this does not hold when the initial conditions are chosen at random. The proof of convergence investigates the interaction between the two layers of the network. Our results highlight the importance of using symmetry in the design of neural networks.
Variational inference for diffusion modulated Cox processes
Authors: Prateek Jaiswal, Harsha Honnappa, Vinayak Rao

This paper proposes a stochastic variational inference (SVI) method for computing an approximate posterior path measure of a Cox process. These processes are widely used in natural and physical sciences, engineering and operations research, and represent a non-trivial model of a wide array of phenomena. In our work, we model the stochastic intensity as the solution of a diffusion stochastic differential equation (SDE), and our objective is to infer the posterior, or smoothing, measure over the paths given Poisson process realizations. We first derive a system of stochastic partial differential equations (SPDE) for the pathwise smoothing posterior density function, a non-trivial result, since the standard solution of SPDEs typically involves an It\^o stochastic integral, which is not defined pathwise. Next, we propose an SVI approach to approximating the solution of the system. We parametrize the class of approximate smoothing posteriors using a neural network, derive a lower bound on the evidence of the observed point process sample-path, and optimize the lower bound using stochastic gradient descent (SGD). We demonstrate the efficacy of our method on both synthetic and real-world problems, and demonstrate the advantage of the neural network solution over standard numerical solvers.
Using Hindsight to Anchor Past Knowledge in Continual Learning
Authors: Arslan Chaudhry, Albert Gordo, David Lopez-Paz, Puneet K. Dokania, Philip Torr

In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forget previously acquired knowledge. To address such catastrophic forgetting, state-of-the-art continual learning methods implement different types of experience replay, re-learning on past data stored in a small buffer known as episodic memory. In this work, we complement experience replay with a meta-learning technique that we call anchoring: the learner updates its knowledge on the current task, while keeping predictions on some anchor points of past tasks intact. These anchor points are learned using gradient-based optimization as to maximize forgetting of the current task, in hindsight, when the learner is fine-tuned on the episodic memory of past tasks. Experiments on several supervised learning benchmarks for continual learning demonstrate that our approach improves the state of the art in terms of both accuracy and forgetting metrics and for various sizes of episodic memories.
PARS: PSEUDO-LABEL AWARE ROBUST SAMPLE SELECTION FOR LEARNING WITH NOISY LABELS
Authors: Arushi Goel, Yunlong Jiao, Jordan Massiah

Acquiring accurate labels on large-scale datasets is both time consuming and expensive. To reduce the dependency of deep learning models on learning from clean labeled data, several recent research efforts are focused on learning with noisy labels. These methods typically fall into three design categories to learn a noise robust model: sample selection approaches, noise robust loss functions, or label correction methods. In this paper, we propose PARS: Pseudo-Label Aware Robust Sample Selection, a hybrid approach that combines the best from all three worlds in a joint-training framework to achieve robustness to noisy labels. Specifically, PARS exploits all training samples using both the raw/noisy labels and estimated/refurbished pseudo-labels via self-training, divides samples into an ambiguous and a noisy subset via loss analysis, and designs label-dependent noise-aware loss functions for both sets of filtered labels. Results show that PARS significantly outperforms the state of the art on extensive studies on the noisy CIFAR-10 and CIFAR-100 datasets, particularly on challenging high-noise and low-resource settings. In particular, PARS achieved an absolute 12% improvement in test accuracy on the CIFAR-100 dataset with 90% symmetric label noise, and an absolute 27% improvement in test accuracy when only 1/5 of the noisy labels are available during training as an additional restriction. On a real-world noisy dataset, Clothing1M, PARS achieves competitive results to the state of the art.
Learning Entailment-Based Sentence Embeddings from Natural Language Inference
Authors: Rabeeh Karimi Mahabadi*, Florian Mai*, James Henderson

Large datasets on natural language inference are a potentially valuable resource for inducing semantic representations of natural language sentences.  But in many such models the embeddings computed by the sentence encoder goes through an MLP-based interaction layer before predicting its label, and thus some of the information about textual entailment is encoded in the interpretation of sentence embeddings given by this parameterised MLP.  
In this work we propose a simple interaction layer based on predefined entailment and contradiction scores applied directly to the sentence embeddings.  This parameter-free interaction model achieves results on natural language inference competitive with MLP-based models, demonstrating that the trained sentence embeddings directly represent the information needed for textual entailment, and the inductive bias of this model leads to better generalisation to other related datasets.
Are BERT Families Zero-Shot Learners? A Study on Their Potential and Limitations
Authors: Yue Wang, Lijun Wu, xiaobo liang, Juntao Li, Min Zhang

Starting from the resurgence of deep learning, language models (LMs) have never been so popular. Through simply increasing model scale and data size, large LMs pre-trained with self-supervision objectives demonstrate awe-inspiring results on both task performance and generalization. At the early stage, supervised fine-tuning is indispensable in adapting pre-trained language models (PLMs) to downstream tasks. Later on, the sustained growth of model capacity and data size, as well as newly presented pre-training techniques, make the PLMs perform well under the few-shot setting, especially in the recent paradigm of prompt-based learning. After witnessing the success of PLMs for few-shot tasks, we propose to further study the potential and limitations of PLMs for the zero-shot setting. We utilize 3 models from the most popular BERT family to launch the empirical study on 20 different datasets. We are surprised to find that a simple Multi-Null Prompting (without manually/automatically created prompts) strategy can yield very promising results on a few widely-used datasets, e.g., $86.59\%(\pm0.59)$ accuracy on the IMDB dataset, and $86.22\%(\pm2.71)$ accuracy on the Amazon dataset, which outperforms manually created prompts without engineering in achieving much better and stable performance with the accuracy of $74.06\%(\pm13.04)$, $75.54\%(\pm11.77)$ for comparison. However, we also observe some limitations of PLMs under the zero-shot setting, particularly for the language understanding tasks (e.g., GLUE).
Identifying Weights and Architectures of Unknown ReLU Networks
Authors: David Rolnick, Konrad P. Kording

The output of a neural network depends on its parameters in a highly nonlinear way, and it is widely assumed that a network's parameters cannot be identified from its outputs. Here, we show that in many cases it is possible to reconstruct the architecture, weights, and biases of a deep ReLU network given the ability to query the network. ReLU networks are piecewise linear and the boundaries between pieces correspond to inputs for which one of the ReLUs switches between inactive and active states. Thus, first-layer ReLUs can be identified (up to sign and scaling) based on the orientation of their associated hyperplanes. Later-layer ReLU boundaries bend when they cross earlier-layer boundaries and the extent of bending reveals the weights between them. Our algorithm uses this to identify the units in the network and weights connecting them (up to isomorphism). The fact that considerable parts of deep networks can be identified from their outputs has implications for security, neuroscience, and our understanding of neural networks.
Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time
Authors: Zhao Song, Lichen Zhang, Ruizhe Zhang

In the recent years of development of theoretical machine learning, over-parametrization has been shown to be a powerful tool to resolve many fundamental problems, such as the convergence analysis of deep neural network. While many works have been focusing on designing various algorithms for over-parametrized network with one-hidden layer, multiple-hidden layers framework has received much less attention due to the complexity of the analysis, and even fewer algorithms have been proposed. In this work, we initiate the study of the performance of second-order algorithm on multiple-hidden layers over-parametrized neural network. We propose a novel algorithm to train such network, in time subquadratic in the width of the neural network. Our algorithm combines the Gram-Gauss-Newton method, tensor-based sketching techniques and preconditioning.
Explaining Off-Policy Actor-Critic From A Bias-Variance Perspective
Authors: Ting-Han Fan, Peter Ramadge

Off-policy Actor-Critic algorithms have demonstrated phenomenal experimental performance but still require better explanations. To this end, we show its policy evaluation error on the distribution of transitions decomposes into: a Bellman error, a bias from policy mismatch, and a variance term from sampling. By comparing the magnitude of bias and variance, we explain the success of the Emphasizing Recent Experience sampling and 1/age weighted sampling. Both sampling strategies yield smaller bias and variance and are hence preferable to uniform sampling.
HAPPIER: Hierarchical Polyphonic Music Generative RNN
Authors: Tianyang Zhao, Xiaoxuan Ma, Honglin Ma, Yizhou Wang

Generating polyphonic music with coherent global structure is a major challenge for automatic composition algorithms. The primary difficulty arises due to the inefficiency of models to recognize underlying patterns beneath music notes across different levels of time scales and remain long-term consistency while composing. Hierarchical architectures can capture and represent learned patterns in different temporal scales and maintain consistency over long time spans, and this corresponds to the hierarchical structure in music. Motivated by this, focusing on leveraging the idea of hierarchical models and improve them to fit the sequence modeling problem, our paper proposes HAPPIER: a novel HierArchical PolyPhonic musIc gEnerative RNN. In HAPPIER, A higher `measure level' learns correlations across measures and patterns for chord progressions, and a lower `note level' learns a conditional distribution over the notes to generate within a measure. The two hierarchies operate at different clock rates: the higher one operates on a longer timescale and updates every measure, while the lower one operates on a shorter timescale and updates every unit duration. The two levels communicate with each other, and thus the entire architecture is trained jointly end-to-end by back-propagation. HAPPIER, profited from the strength of the hierarchical structure, generates polyphonic music with long-term dependencies compared to the state-of-the-art methods.
Proper Measure for Adversarial Robustness
Authors: Hyeongji Kim, Ketil Malde

This paper analyzes the problems of adversarial accuracy and adversarial training. We argue that standard adversarial accuracy fails to properly measure the robustness of classifiers. Its definition has a tradeoff with standard accuracy even when we neglect generalization. In order to handle the problems of the standard adversarial accuracy, we introduce a new measure for the robustness of classifiers called genuine adversarial accuracy. It can measure the adversarial robustness of classifiers without trading off accuracy on clean data and accuracy on the adversarially perturbed samples. In addition, it does not favor a model with invariance-based adversarial examples, samples whose predicted classes are unchanged even if the perceptual classes are changed. We prove that a single nearest neighbor (1-NN) classifier is the most robust classifier according to genuine adversarial accuracy for given data and a norm-based distance metric when the class for each data point is unique. Based on this result, we suggest that using poor distance metrics might be one factor for the tradeoff between test accuracy and $l_p$ norm-based test adversarial robustness.
Efficient Exploration via State Marginal Matching
Authors: Lisa Lee, Benjain Eysenbach, Emilio Parisotto, Erix Xing, Sergey Levine, Ruslan Salakhutdinov

Reinforcement learning agents need to explore their unknown environments to solve the tasks given to them. The Bayes optimal solution to exploration is intractable for complex environments, and while several exploration methods have been proposed as approximations, it remains unclear what underlying objective is being optimized by existing exploration methods, or how they can be altered to incorporate prior knowledge about the task. Moreover, it is unclear how to acquire a single exploration strategy that will be useful for solving multiple downstream tasks. We address these shortcomings by learning a single exploration policy that can quickly solve a suite of downstream tasks in a multi-task setting, amortizing the cost of learning to explore. We recast exploration as a problem of State Marginal Matching (SMM), where we aim to learn a policy for which the state marginal distribution matches a given target state distribution, which can incorporate prior knowledge about the task. We optimize the objective by reducing it to a two-player, zero-sum game between a state density model and a parametric policy. Our theoretical analysis of this approach suggests that prior exploration methods do not learn a policy that does distribution matching, but acquire a replay buffer that performs distribution matching, an observation that potentially explains these prior methods' success in single-task settings. On both simulated and real-world tasks, we demonstrate that our algorithm explores faster and adapts more quickly than prior methods.
Addressing the Topological Defects of Disentanglement
Authors: Diane Bouchacourt, Mark Ibrahim, Stephane Deny

A core challenge in Machine Learning is to disentangle natural factors of variation in data (e.g. object shape vs pose).  A popular approach to disentanglement consists in learning to map each of these factors to distinct subspaces of a model's latent representation. However, this approach has shown limited empirical success to date. Here, we show that this approach to disentanglement introduces topological defects (i.e. discontinuities in the encoder) for a broad family of transformations acting on images ---encompassing simple affine transformations such as rotations and translations.  Moreover, motivated by classical results from group representation theory, we propose an alternative, more flexible approach to disentanglement which relies on distributed equivariant operators, potentially acting on the entire latent space. We theoretically and empirically demonstrate the effectiveness of our approach to disentangle affine transformations. Our work lays a theoretical foundation for the recent success of a new generation of models using distributed operators for disentanglement (see Discussion).
Augmenting Self-attention with Persistent Memory
Authors: Sainbayar Sukhbaatar, Edouard Grave, Guillaume Lample, Herve Jegou, Armand Joulin

Transformer networks have lead to important progress in language modeling and machine translation. These models include two consecutive modules, a feed-forward layer and a self-attention layer. The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers. Building upon this intuition, we propose a new model that solely consists of attention layers. More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer. Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer. Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks.
MSFM: Multi-Scale Fusion Module for Object Detection
Authors: Xuesong Wang, Caisheng Wang

Feature fusion is beneficial to object detection tasks in two folds. On one hand, detail and position information can be combined with semantic information when high and low-resolution features from shallow and deep layers are fused. On the other hand, objects can be detected in different scales, which improves the robustness of the framework. In this work, we present a Multi-Scale Fusion Module (MSFM) that extracts both detail and semantical information from a single input but at different scales within the same layer. Specifically, the input of the module will be resized into different scales on which position and semantic information will be processed, and then they will be rescaled back and combined with the module input. The MSFM is lightweight and can be used as a drop-in layer to many existing object detection frameworks. Experiments show that MSFM can bring +2.5% mAP improvement with only 2.4M extra parameters on Faster R-CNN with ResNet-50 FPN backbone on COCO Object Detection minival set, outperforming that with ResNet-101 FPN backbone without the module which obtains +2.0% mAP with 19.0M extra parameters. The best resulting model achieves a 45.7% mAP on test-dev set. Code will be available.
Towards fast and effective single-step adversarial training
Authors: Pau de Jorge, Adel Bibi, Riccardo Volpi, Amartya Sanyal, Philip Torr, Grégory Rogez, Puneet K. Dokania

Recently, Wong et al. (2020) showed adversarial training with single-step FGSM leads  to  a  characteristic  failure  mode  named catastrophic  overfitting (CO),  in which  a  model  becomes  suddenly  vulnerable  to  multi-step  attacks.   Moreover, they showed adding a random perturbation prior to FGSM (RS-FGSM) seemed to be sufficient to prevent CO. However, Andriushchenko & Flammarion (2020) observed that RS-FGSM still leads to CO for larger perturbations and argue that the  only  contribution  of  the  random  step  is  to  reduce  the  magnitude  of  the  attacks.  They suggest a regularizer (GradAlign) that avoids CO but is significantly more expensive than RS-FGSM. In this work,  we methodically revisit the role of noise and clipping in single-step adversarial training.  Contrary to previous intuitions, we find that not clipping the perturbation around the clean sample and using a stronger noise is highly effective in avoiding CO for large perturbation radii,  despite leading to an increase in the magnitude of the attacks.   Based on these observations, we propose a method called Noise-FGSM (N-FGSM), which attacks noise-augmented samples directly using a single-step. Empirical analyses on a large suite of experiments show that N-FGSM is able to match or surpass the performance of GradAlign while achieving a 3x speed-up.
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION
Authors: Jiamian Wang, Yulun Zhang, Xin Yuan, Yun Fu, ZHIQIANG TAO

As the inverse process of snapshot compressive imaging, the hyperspectral image (HSI) reconstruction takes the 2D measurement as input and posteriorly retrieves the captured 3D spatial-spectral signal. Built upon several assumptions, numerous sophisticated neural networks have come to the fore in this task. Despite their prosperity under experimental settings, it's still extremely challenging for existing networks to achieve high-fidelity reconstructive quality while maximizing the reconstructive efficiency (computational efficiency and power occupation), which prohibits their further deployment in practical applications. In this paper, we firstly conduct a retrospective analysis on aforementioned assumptions, through which we indicate the imminent aspiration for an authentically practical-oriented network in reconstructive community. By analysing the effectiveness and limitations of the widely-used reconstructive backbone U-Net, we propose a Simple Reconstruction Network, namely SRN, just based on some popular techniques, e.g., scale/spectral-invariant learning and identity connection. It turns out, under current conditions, such a pragmatic solution outperforms existing reconstructive methods by an obvious margin and maximize the reconstructive efficiency concretely. We hope the proposed SRN can further contribute to the cutting-edge reconstructive methods as a promising backbone, and also benefit the realistic tasks, i.e., real-time/high-resolution HSI reconstruction, solely as a baseline.
Approximate Birkhoff-von-Neumann decomposition: a differentiable approach
Authors: Andrés Hoyos-Idrobo

The Birkhoff-von-Neumann (BvN) decomposition is a standard tool used to draw permutation matrices from a doubly stochastic (DS) matrix. The BvN decomposition represents such a DS matrix as a convex combination of several permutation matrices. Currently, most algorithms to compute the BvN decomposition employ either greedy strategies or custom-made heuristics. In this paper, we present a novel differentiable approach to approximate the BvN decomposition. Our algorithm builds upon recent advances in Riemannian optimization on Birkhoff polytopes. We offer an empirical evaluation of this approach in the fairness of exposure in rankings, where we show that the outcome of our method behaves similarly to greedy algorithms. Our approach is an excellent addition to existing methods for sampling from DS matrices, such as sampling from a Gumbel-Sinkhorn distribution. However, our approach is better suited for applications where the latency in prediction time is a constraint. Indeed, we can generally precompute an approximated BvN decomposition offline. Then, we select a permutation matrix at random with probability proportional to its coefficient. Finally, we provide an implementation of our method.
rQdia: Regularizing Q-Value Distributions With Image Augmentation
Authors: Samuel Lerman, Jing Bi, Chenliang Xu

rQdia (pronounced “Arcadia”) regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning. With a simple auxiliary loss, that equalizes these distributions via MSE, rQdia boosts DrQ and SAC on 9/12 and 10/12 tasks respectively in the MuJoCo Continuous Control Suite from pixels, and Data-Efficient Rainbow on 18/26 Atari Arcade environments. Gains are measured in both sample efficiency and longer-term training. Moreover, the addition of rQdia finally propels model-free continuous control from pixels over the state encoding baseline. Additional results, namely more random seeds, pending.