nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge Hypergraphs: Prediction Beyond Binary Relations
Authors: Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole

A Knowledge Hypergraph is a knowledge base where relations are defined on two or more entities. In this work, we introduce two embedding-based models that perform link prediction in knowledge hypergraphs:
(1) HSimplE is a shift-based method that is inspired by an existing model operating on knowledge graphs, in which the representation of an entity is a function of its position in the relation, and (2) HypE is a convolution-based method which disentangles the representation of an entity from its position in the relation. We test our models on two new knowledge hypergraph datasets that we obtain from Freebase, and show that both HSimplE and HypE are more effective in predicting links in knowledge hypergraphs than the proposed baselines and existing methods.
Our experiments show that HypE outperforms HSimplE when trained with fewer parameters and when tested on samples that contain at least one entity in a position never encountered during training.
Multi-modal Self-Supervision from Generalized Data Transformations
Authors: Mandela Patrick, Yuki Asano, Polina Kuznetsova, Ruth Fong, Joao F. Henriques, Geoffrey Zweig, Andrea Vedaldi

In the image domain, excellent representation can be learned by inducing invariance to content-preserving transformations, such as image distortions. In this paper, we show that, for videos, the answer is more complex, and that better results can be obtained by accounting for the interplay between invariance, distinctiveness, multiple modalities and time.  We introduce Generalized Data Transformations (GDTs) as a way to capture this interplay. GDTs reduce most previous self-supervised approaches to a choice of data transformations, even when this was not the case in the original formulations. They also allow to choose whether the representation should be invariant or distinctive w.r.t. each effect and tell which combinations are valid, thus allowing us to explore the space of combinations systematically. We show in this manner that being invariant to certain transformations and distinctive to others is critical to learning effective video representations, improving the state-of-the-art by a large margin, and even surpassing supervised pretraining. We demonstrate results on a variety of downstream video and audio classification and retrieval tasks, on datasets such as HMDB-51, UCF-101, DCASE2014, ESC-50 and VGG-Sound. In particular, we achieve new state-of-the-art accuracies of 72.8% on HMDB-51 and 95.2% on UCF-101.
A Zest of LIME: Towards Architecture-Independent Model Distances
Authors: Hengrui Jia, Hongyu Chen, Jonas Guan, Ali Shahin Shamsabadi, Nicolas Papernot

Definitions of the distance between two machine learning models either characterize the similarity of the models' predictions or of their weights. While similarity of weights is attractive because it implies similarity of predictions in the limit, it suffers from being inapplicable to comparing models with different architectures. On the other hand, the similarity of predictions is broadly applicable but depends heavily on the choice of model inputs during comparison. In this paper, we instead propose to compute distance between black-box models by comparing their Local Interpretable Model-Agnostic Explanations (LIME). To compare two models, we take a reference dataset, and locally approximate the models on each reference point with linear models trained by LIME. We then compute the cosine distance between the concatenated weights of the linear models. This yields an approach that is both architecture-independent and possesses the benefits of comparing models in weight space. We empirically show that our method, which we call Zest, can be applied to two problems that require measurements of model similarity: detecting model stealing and machine unlearning.
Learning Generative Models with Locally Disentangled Latent Factors
Authors: Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio, Ioannis Mitliagkas

One of the most successful techniques in generative models has been decomposing a complicated generation task into a series of simpler generation tasks.  For example, generating an image at a low resolution and then learning to refine that into a high resolution image often improves results substantially.  Here we explore a novel strategy for decomposing generation for complicated objects in which we first generate latent variables which describe a subset of the observed variables, and then map from these latent variables to the observed space.  We show that this allows us to achieve decoupled training of complicated generative models and present both theoretical and experimental results supporting the benefit of such an approach.
The problem with DDPG: understanding failures in deterministic environments with sparse rewards
Authors: Guillaume Matheron, Olivier Sigaud, Nicolas Perrin

In environments with continuous state and action spaces, state-of-the-art actor-critic reinforcement learning algorithms can solve very complex problems, yet can also fail in environments that seem trivial, but the reason for such failures is still poorly understood. In this paper, we contribute a formal explanation of these failures in the particular case of sparse reward and deterministic environments. First, using a very elementary control problem, we illustrate that the learning process can get
stuck into a fixed point corresponding to a poor solution. Then, generalizing from the studied example, we provide a detailed analysis of the underlying mechanisms which results in a new understanding of one of the convergence regimes of these algorithms. The resulting perspective casts a new light on already existing solutions to the issues we have highlighted, and suggests other potential approaches.
Demystifying How Self-Supervised Features Improve Training from Noisy Labels
Authors: Hao Cheng, Zhaowei Zhu, Xing Sun, Yang Liu

The advancement of self-supervised learning (SSL) motivates researchers to apply SSL on other tasks such as learning with noisy labels. Recent literature indicates that methods built on SSL features can substantially improve the performance of learning with noisy labels. Nonetheless, the deeper reasons why (and how) SSL features benefit the training from noisy labels are less understood. In this paper, we study why and how self-supervised features help networks resist label noise using both theoretical analyses and numerical experiments. Our result shows that, given a quality encoder pre-trained from SSL, a simple linear layer trained by the cross-entropy loss is theoretically robust to symmetric label noise. Further, we provide insights for how knowledge distilled from SSL features can alleviate the over-fitting problem. We hope our work provides a better understanding for learning with noisy labels from the perspective of self-supervised learning and can potentially serve as a guideline for further research.
Modifying memories in a Recurrent Neural Network Unit
Authors: Vlad Velici, Adam Prügel-Bennett

Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data. We introduce the concept of modifying the cell state (memory) of LSTMs using rotation matrices parametrised by a new set of trainable weights. This addition shows significant increases of performance on some of the tasks from the bAbI dataset.
Symmetry-driven graph neural networks
Authors: Francesco Farina, Emma Slade

Exploiting symmetries and invariance in data is a powerful, yet not fully exploited, way to achieve better generalisation with more
efficiency.  In this paper, we introduce two graph network architectures that are equivariant to several types of transformations affecting the node coordinates.  First, we build equivariance to any transformation in the coordinate embeddings that preserves the distance between neighbouring nodes, allowing for equivariance to the Euclidean group. Then, we introduce angle attributes to build equivariance to any angle preserving transformation - thus, to the conformal group.  Thanks to their equivariance properties, the proposed models can be vastly more data efficient with respect to classical graph architectures, intrinsically equipped with a better inductive bias and better at generalising.  We demonstrate these capabilities on a synthetic dataset composed of $n$-dimensional geometric objects.  Additionally, we provide examples of their limitations when (the right) symmetries are not present in the data.
Wasserstein Adversarial Regularization (WAR) on label noise
Authors: Bharath Damodaran, Kilian Fatras, Sylvain Lobry, Rémi Flamary, Devis Tuia, Nicolas Courty

Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning robust classifiers in presence of noisy data. To achieve this goal, we propose a new adversarial regularization scheme based on the Wasserstein distance.  Using this distance allows taking into account specific relations between classes by leveraging the geometric properties of the labels space.  Our Wasserstein Adversarial Regularization (WAR) encodes a selective regularization, which promotes smoothness of the classifier between some classes, while preserving sufficient complexity of the decision boundary between others. We first discuss how and why adversarial regularization can be used in the context of label noise and then show the effectiveness of our method on five datasets corrupted with noisy labels: in both benchmarks and real datasets, WAR outperforms the state-of-the-art
competitors.
Learn to Pay Attention
Authors: Saumya Jetley, Nicholas A. Lord, Namhoon Lee, Philip H. S. Torr

We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map. Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parametrised by the score matrices, must alone be used for classification. Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values. Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter. Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets. When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset. We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.
Adaptive Learning of Tensor Network Structures
Authors: Meraj Hashemizadeh, Michelle Liu, Jacob Miller, Guillaume Rabusseau

Tensor Networks (TN) offer a powerful framework to efficiently represent very high-dimensional objects. TN have recently shown their potential for machine learning applications and offer a unifying view of common tensor decomposition models such as Tucker, tensor train (TT) and tensor ring (TR). However, identifying the best tensor network structure from data for a given task is challenging. In this work, we leverage the TN formalism to develop a generic and efficient adaptive algorithm to jointly learn the structure and the parameters of a TN from data. Our method is based on a simple greedy approach starting from a rank one tensor and successively identifying the most promising tensor network edges for small rank increments. Our algorithm can adaptively identify TN structures with small number of parameters that effectively optimize any differentiable objective function. Experiments on tensor decomposition, tensor completion and model compression tasks demonstrate the effectiveness of the proposed algorithm. In particular, our method outperforms the state-of-the-art evolutionary topology search [Li and Sun, 2020] for tensor decomposition of images (while being orders of magnitude faster) and finds efficient tensor network structures to compress neural networks outperforming popular TT based approaches [Novikov et al., 2015].
On the Convergence and Robustness of Batch Normalization
Authors: Yongqiang Cai, Qianxiao Li, Zuowei Shen

Despite its empirical success, the theoretical underpinnings of the stability, convergence and acceleration properties of batch normalization (BN) remain elusive. In this paper, we attack this problem from a modelling approach, where we perform thorough theoretical analysis on BN applied to simplified model: ordinary least squares (OLS). We discover that gradient descent on OLS with BN has interesting properties, including a scaling law, convergence for arbitrary learning rates for the weights, asymptotic acceleration effects, as well as insensitivity to choice of learning rates. We then demonstrate numerically that these findings are not specific to the OLS problem and hold qualitatively for more complex supervised learning problems. This points to a new direction towards uncovering the mathematical principles that underlies batch normalization.
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING
Authors: Xin Zhang, Weixiao Huang, Renjie Liao, Yanhua Li

Imitation learning aims to inversely learn a policy from expert demonstrations, which has been extensively studied in the literature for both single-agent setting with Markov decision process (MDP) model, and multi-agent setting with Markov game (MG) model. However, existing approaches for general multi-agent Markov games are not applicable to multi-agent extensive Markov games, where agents make asynchronous decisions following a certain order, rather than simultaneous decisions. We propose a novel framework for asynchronous multi-agent generative adversarial imitation learning (AMAGAIL) under general extensive Markov game settings, and the learned expert policies are proven to guarantee subgame perfect equilibrium (SPE), a more general and stronger equilibrium than Nash equilibrium (NE). The experiment results demonstrate that compared to state-of-the-art baselines, our AMAGAIL model can better infer the policy of each expert agent using their demonstration data collected from asynchronous decision-making scenarios (i.e., extensive Markov games).
Improving Neural Language Models with a Continuous Cache
Authors: Edouard Grave, Armand Joulin, Nicolas Usunier

We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented networks.
How to deal with missing data in supervised deep learning?
Authors: Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

The issue of missing data in supervised learning has been largely overlooked, especially in the deep learning community. We investigate strategies to adapt neural architectures for handling missing values. Here, we focus on regression and classification problems where the features are assumed to be missing at random. Of particular interest are schemes that allow reusing as-is a neural discriminative architecture. To address supervised deep learning with missing values, we propose to marginalize over missing values in a joint model of covariates and outcomes. Thereby, we leverage both the flexibility of deep generative models to describe the distribution of the covariates and the power of purely discriminative models to make predictions. More precisely, a deep latent variable model can be learned jointly with the discriminative model, using importance-weighted variational inference, essentially using importance sampling to mimick averaging over multiple imputations. In low-capacity regimes, or when the discriminative model has a strong inductive bias, we find that our hybrid generative/discriminative approach generally outperforms single imputations methods.
Topic-aware Contextualized Transformers
Authors: Ruiying Lu, Bo Chen, Dan dan Guo, Dongsheng Wang, Mingyuan Zhou

Training on disjoint fixed-length segments, Transformers successfully transform static word embeddings into contextualized word representations. However, they often restrict the context of a token to the segment it resides in and hence neglect the flow of contextual information across segments, failing to capture longer-term dependencies beyond the predefined segment length. This paper uses a probabilistic deep topic model to provide contextualized embeddings at both the token and segment levels. It also introduces topic self-attention and a contextual next-word embedding guided topic select-attention, injecting contextualized topic information into Transformer-based architectures. Moving beyond conventional Transformers that ignore longer-range word dependencies and contextualize their word representations at the segment level, the proposed method not only captures global semantic coherence of all segments and global word concurrence patterns, but also enriches the representation of each token by adapting it to its local context, which is not limited to the segment it resides in and can be flexibly defined according to the task. Experiments on various corpora show that adding only a few extra parameters, the proposed topic-aware contextualized transformers consistently outperform their conventional counterparts, and can be used to generate coherent sentences and paragraphs.
Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model
Authors: Alex X. Lee, Anusha Nagabandi, Pieter Abbeel, Sergey Levine

Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations. However, these kinds of observation spaces present a number of  challenges in practice, since the policy must now solve two problems: a representation learning problem, and a task learning problem. In this paper, we aim to explicitly learn representations that can accelerate reinforcement learning from images. We propose the stochastic latent actor-critic (SLAC) algorithm: a sample-efficient and high-performing RL algorithm for learning policies for complex continuous control tasks directly from high-dimensional image inputs. SLAC learns a compact latent representation space using a stochastic sequential latent variable model, and then learns a critic model within this latent space. By learning a critic within a compact state space, SLAC can learn much more efficiently than standard RL methods. The proposed model improves performance substantially over alternative representations as well, such as variational autoencoders. In fact, our experimental evaluation demonstrates that the sample efficiency of our resulting method is comparable to that of model-based RL methods that directly use a similar type of model for control. Furthermore, our method outperforms both model-free and model-based alternatives in terms of final performance and sample efficiency, on a range of difficult image-based control tasks. Our code and videos of our results are available at our website.
On the Estimation Bias in Double Q-Learning
Authors: Zhizhou Ren, Guangxiang Zhu, Beining Han, Jianglun Chen, Chongjie Zhang

Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operator. Its variants in the deep Q-learning paradigm have shown great promise in producing reliable value prediction and improving learning performance. However, as shown by prior work, double Q-learning is not fully unbiased and still suffers from underestimation bias. In this paper, we show that such underestimation bias may lead to multiple non-optimal fixed points under an approximated Bellman operation. To address the concerns of converging to non-optimal stationary solutions, we propose a simple and effective approach as a partial fix for underestimation bias in double Q-learning. This approach leverages real returns to bound the target value. We extensively evaluate the proposed method in the Atari benchmark tasks and demonstrate its significant improvement over baseline algorithms.
Identifying Bias in AI using Simulation
Authors: Daniel McDuff, Roger Cheng, Ashish Kapoor

Machine learned models exhibit bias, often because the datasets used to train them are biased. This presents a serious problem for the deployment of such technology, as the resulting models might perform poorly on populations that are minorities within the training set and ultimately present higher risks to them. We propose to use high-fidelity computer simulations to interrogate and diagnose biases within ML classifiers. We present a framework that leverages Bayesian parameter search to efficiently characterize the high dimensional feature space and more quickly identify weakness in performance. We apply our approach to an example domain, face detection, and show that it can be used to help identify demographic biases in commercial face application programming interfaces (APIs).
Reconstructing continuous distributions of 3D protein structure from cryo-EM images
Authors: Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the 3D structure of a macromolecule from $10^{4-7}$ noisy and randomly oriented 2D projection images. However, the imaged protein complexes may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of protein dynamics. Here, we introduce a novel method for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of structural heterogeneity. This method encodes structures in Fourier space using coordinate-based deep neural networks, and trains these networks from unlabeled 2D cryo-EM images by combining exact inference over image orientation with variational inference for structural heterogeneity. We demonstrate that the proposed method, termed cryoDRGN, can perform ab-initio reconstruction of 3D protein complexes from simulated and real 2D cryo-EM image data. To our knowledge, cryoDRGN is the first neural network-based approach for cryo-EM reconstruction and the first end-to-end method for directly reconstructing continuous ensembles of protein structures from cryo-EM images.