nils-herrmann/clean_scirepeval huggingface dataset:
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Authors: Jiechao Xiong, Qing Wang, Zhuoran Yang, Peng Sun, Yang Zheng, Lei Han, Haobo Fu, Xiangru Lian, Carson Eisenach, Haichuan Yang, Emmanuel Ekwedike, Bei Peng, Haoyue Gao, Tong Zhang, Ji Liu, Han Liu

Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either
discrete or continuous space. Motivated by the project of design Game AI for King of Glory
(KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous
hybrid action space. To directly apply existing DLR frameworks, existing approaches
either approximate the hybrid space by a discrete set or relaxing it into a continuous set, which is
usually less efficient and robust. In this paper, we propose a parametrized deep Q-network (P-DQN)
for the hybrid action space without approximation or relaxation. Our algorithm combines DQN and
DDPG and can be viewed as an extension of the DQN to hybrid actions. The empirical study on the
game KOG validates the efficiency and effectiveness of our method.
Sparse Attention with Learning to Hash
Authors: Zhiqing Sun, Yiming Yang, Shinjae Yoo

Transformer has become ubiquitous in sequence modeling tasks. As a key component of Transformer, self-attention does not scale to long sequences due to its quadratic time and space complexity with respect to the sequence length. To tackle this problem, recent work developed dynamic attention sparsification techniques based on Approximate Nearest Neighbor (ANN) methods, where similar queries and keys are allocated to the same hash bucket with high probability. However, the effectiveness of those ANN methods relies on the assumption that queries and keys should lie in the same space, which is not well justified. Besides, some of the ANN methods such as Locality-Sensitive Hashing (LSH) are randomized and cannot fully utilize the available real data distributions. To overcome these issues, this paper proposes a new strategy for sparse attention, namely LHA (Learning-to-Hash Attention), which directly learns separate parameterized hash functions for queries and keys, respectively. Another advantage of LHA is that it does not impose extra constraints for queries and keys, which makes it applicable to the wide range of pre-trained Transformer models. Our experiments on evaluation of the WikiText-103 dataset for language modeling, the GLUE benchmark for natural language understanding, and the Lang-Range-Arena benchmark for multiple tasks (text/image classification, retrieval, etc.) show the superior performance of LHA over other strong Transformer variants.
Safe Neurosymbolic Learning with Differentiable Symbolic Execution
Authors: Chenxi Yang, Swarat Chaudhuri

We study the problem of learning verifiably safe parameters for programs that use neural networks as well as symbolic, human-written code. Such neurosymbolic programs arise in many safety-critical domains. However, because they need not be differentiable, it is hard to learn their parameters using existing gradient-based approaches to safe learning. Our method, Differentiable Symbolic Execution (DSE), samples control flow paths in a program, symbolically constructs worst-case "safety loss" along these paths, and backpropagates the gradients of these losses through program operations using a generalization of the REINFORCE estimator. We evaluate the method on a mix of synthetic tasks and real-world benchmarks. Our experiments show that DSE significantly outperforms the state-of-the-art DiffAI method on these tasks.
Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases
Authors: Mengying Sun, Inci M. Baytas, Zhangyang Wang, Jiayu Zhou

Over the past decade a wide spectrum of machine learning models have been developed to model the neurodegenerative diseases, associating biomarkers, especially non-intrusive neuroimaging markers, with key clinical scores measuring the cognitive status of patients. Multi-task learning (MTL) has been extensively explored in these studies to address challenges associated to high dimensionality and small cohort size. However, most existing MTL approaches are based on linear models and suffer from two major limitations: 1) they cannot explicitly consider upper/lower bounds in these clinical scores; 2) they lack the capability to capture complicated non-linear effects among the variables. In this paper, we propose the Subspace Network, an efficient deep modeling approach for non-linear multi-task censored regression. Each layer of the subspace network performs a multi-task censored regression to improve upon the predictions from the last layer via sketching a low-dimensional subspace to perform knowledge transfer among learning tasks. We show that under mild assumptions, for each layer the parametric subspace can be recovered using only one pass of training data. In addition, empirical results demonstrate that the proposed subspace network quickly picks up correct parameter subspaces, and outperforms state-of-the-arts in predicting neurodegenerative clinical scores using information in brain imaging.
Hierarchically Regularized Deep Forecasting
Authors: Biswajit Paria, Rajat Sen, Amr Ahmed, Abhimanyu Das

Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy. The main challenge is to exploit the hierarchical correlations to simultaneously obtain good prediction accuracy for time series at different levels of the hierarchy. In this paper, we propose a new approach for hierarchical forecasting which consists of two components. First, decomposing the time series along a global set of basis time series and modeling hierarchical constraints using the coefficients of the basis decomposition. And second, using a linear autoregressive model with coefficients that vary with time. Unlike past methods, our approach is scalable (inference for a specific time series only needs access to its own history) while also modeling the hierarchical structure via (approximate) coherence constraints among the time series forecasts. We experiment on several public datasets and demonstrate significantly improved overall performance on forecasts at different levels of the hierarchy, compared to existing state-of-the-art hierarchical models.
Meta-Learning by Hallucinating Useful Examples
Authors: Yu-Xiong Wang, Yuki Uchiyama, Martial Hebert, Karteek Alahari

Learning to hallucinate additional examples has recently been shown as a promising direction to address few-shot learning tasks, which aim to learn novel concepts from very few examples. The hallucination process, however, is still far from generating effective samples for learning. In this work, we investigate two important requirements for the hallucinator --- (i) precision: the generated examples should lead to good classifier performance, and (ii) collaboration: both the hallucinator and the classification component need to be trained jointly. By integrating these requirements as novel loss functions into a general meta-learning with hallucination framework, our model-agnostic PrecisE Collaborative hAlluciNator (PECAN) facilitates data hallucination to improve the performance of new classification tasks. Extensive experiments demonstrate state-of-the-art performance on competitive miniImageNet and ImageNet based few-shot benchmarks in various scenarios.
A comparison of second-order methods for deep convolutional neural networks
Authors: Patrick H. Chen, Cho-jui Hsieh

Despite many second-order methods have been proposed to train neural networks, most of the results were done on smaller single layer fully connected networks, so we still cannot conclude whether it's useful in training deep convolutional networks. In this study, we conduct extensive experiments to answer the question "whether second-order method is useful for deep learning?". In our analysis, we find out although currently second-order methods are too slow to be applied in practice, it can reduce training loss in fewer number of iterations compared with SGD. In addition, we have the following interesting findings: (1) When using a large batch size, inexact-Newton methods will converge much faster than SGD. Therefore inexact-Newton method could be a better choice in distributed training of deep networks. (2) Quasi-newton methods are competitive with SGD even when using ReLu activation function (which has no curvature) on residual networks. However, current methods are too sensitive to parameters and not easy to tune for different settings. Therefore, quasi-newton methods with more self-adjusting mechanisms might be more useful than SGD in training deeper networks.
How Does Mixup Help With Robustness and Generalization?
Authors: Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou

Mixup is a popular data augmentation technique based on on convex combinations of pairs of examples and their labels. This simple technique has shown to substantially improve both the model's robustness as well as the generalization of the trained model. However,  it is not well-understood why such improvement occurs. In this paper, we provide theoretical analysis to demonstrate how using Mixup in training helps model robustness and generalization. For robustness, we show that minimizing the Mixup loss corresponds to approximately minimizing an upper bound of the adversarial loss. This explains why models obtained by Mixup training exhibits robustness to several kinds of adversarial attacks such as Fast Gradient Sign Method (FGSM). For generalization, we prove that Mixup augmentation corresponds to a specific type of data-adaptive regularization which reduces overfitting. Our analysis provides new insights and a framework to understand Mixup.
Unsupervised Audiovisual Synthesis via Exemplar Autoencoders
Authors: Kangle Deng, Aayush Bansal, Deva Ramanan

We present an unsupervised approach that converts the input speech of any individual into audiovisual streams of potentially-infinitely many output speakers. Our approach builds on simple autoencoders that project out-of-sample data onto the distribution of the training set. We use exemplar autoencoders to learn the voice, stylistic prosody, and visual appearance of a specific target exemplar speech. In contrast to existing methods, the proposed approach can be easily extended to an arbitrarily large number of speakers and styles using only 3 minutes of target audio-video data, without requiring any training data for the input speaker. To do so, we learn audiovisual bottleneck representations that capture the structured linguistic content of speech. We outperform prior approaches on both audio and video synthesis.
Succinct Explanations with Cascading Decision Trees
Authors: JIALU ZHANG, Mark Santolucito, Ruzica Piskac

Classic decision tree learning is a binary classification algorithm that constructs models with first-class transparency - every classification has a directly derivable explanation. However, learning decision trees on modern datasets generates large trees, which in turn generate decision paths of excessive depth, obscuring the explanation of classifications. To improve the comprehensibility of classifications, we propose a new decision tree model that we call Cascading Decision Trees. Cascading Decision Trees shorten the size of explanations of classifications, without sacrificing model performance overall. Our key insight is to separate the notion of a decision path and an explanation path. Utilizing this insight, instead of having one monolithic decision tree, we build several smaller decision subtrees and cascade them in sequence. Our cascading decision subtrees are designed to specifically target explanations for positive classifications. This way each subtree identifies the smallest set of features that can classify as many positive samples as possible, without misclassifying any negative samples. Applying cascading decision trees to new samples results in a significantly shorter and succinct explanation, if one of the subtrees detects a positive classification. In that case, we immediately stop and report the decision path of only the current subtree to the user as an explanation for the classification. We evaluate our algorithm on standard datasets, as well as new real-world applications and find that our model shortens the explanation depth by over 40.8\% for positive classifications compared to the classic decision tree model.
Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
Authors: Taewoon Kim

A multilayer perceptron (MLP) is typically made of multiple fully connected layers with nonlinear activation functions. There have been several approaches to make them better (e.g., faster convergence, better convergence limit, etc.). But the researches lack structured ways to test them. We test different MLP architectures by carrying out the experiments on the age and gender datasets. We empirically show that by whitening inputs before every linear layer and adding skip connections, our proposed MLP architecture can result in better performance. Since the whitening process includes dropouts, it can also be used to approximate Bayesian inference. We have open sourced our code, and released models and docker images at https://github.com/anonymous.
Deep Learning of Intrinsically Motivated Options in the Arcade Learning Environment
Authors: Louis Bagot, Kevin Mets, Tom De Schepper, Peter Hellinckx, Steven Latre

Although Intrinsic Motivation allows a Reinforcement Learning agent to generate directed behaviors in an environment, even with sparse or noisy rewards, combining intrinsic and extrinsic rewards is non trivial. As an alternative to the widespread method of a weighted sum of rewards, Explore Options let the agent call an intrinsically motivated agent in order to observe and learn from interesting behaviors in the environment. Such options have only been established for simple tabular cases, and are unfit to high dimensional spaces. In this paper, we propose Deep Explore Options, revising Explore Options within the Deep Reinforcement Learning paradigm to tackle complex visual problems. Deep Explore Options can naturally learn from several unrelated intrinsic rewards, ignore harmful intrinsic rewards, learn to balance exploration, but also isolate exploitative or exploratory behaviors. In order to achieve this, we first introduce J-PER, a new transition-selection algorithm based on the interest of multiple agents. Next, we propose to consider intrinsic reward learning as an auxiliary task, with a resulting architecture achieving $50\%$ faster wall-clock speed and building a stronger, shared representation. We test Deep Explore Options on hard and easy exploration games of the Atari Suite, following a benchmarking study to ensure fairness. Our results show that not only can they learn from multiple intrinsic rewards, they are a very strong alternative to a weighted sum of rewards, convincingly beating the baselines in 4 of the 6 tested environments, and with comparable performances in the other 2.
Learning vector representation of local content and matrix representation of local motion, with implications for V1
Authors: Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu

This paper proposes a representational model for image pair such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the following two components. (1) The vector representations of local contents of images. (2) The matrix representations of local pixel displacements caused by the relative motions between the agent and the objects in the 3D scene. When the image frame undergoes changes due to local pixel displacements, the vectors are multiplied by the matrices that represent the local displacements. Our experiments show that our model can learn to infer local motions. Moreover, the model can learn Gabor-like filter pairs of quadrature phases.
Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup
Authors: Cheng Yang, Shengnan Wang, Chao Yang, Yuechuan Li, Ru He, Jingqiao Zhang

Pre-trained language models, such as BERT, have achieved significant accuracy gain in many natural language processing tasks. Despite its effectiveness, the huge number of parameters makes training a BERT model  computationally very challenging.  In this paper, we propose an efficient multi-stage layerwise training (MSLT) approach to reduce the training time of BERT.  We decompose the whole training process into several stages. The training is started from a small model with only a few encoder layers and we gradually increase the depth of the model by adding new encoder layers. At each stage, we only train the top  (near the output layer)  few encoder layers which are newly added. The parameters of the other layers which have been trained in the previous stages will not be updated in the current stage. In  BERT training, the backward calculation is much more time-consuming than the forward calculation, especially in the distributed training setting in which the backward calculation time further includes the communication time for gradient synchronization. In the proposed training strategy,  only  top few layers  participate  backward calculation, while most  layers  only participate forward calculation. Hence both  the computation  and communication efficiencies are greatly improved.  Experimental results show that the proposed method can greatly reduce the training time without significant performance degradation.
Generative Adversarial Networks using Adaptive Convolution
Authors: Nhat M. Nguyen, Nilanjan Ray

Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator. We argue that this kind of fixed operation is problematic for GANs to model objects that have very different visual appearances. We propose a novel adaptive convolution method that learns the upsampling algorithm based on the local context at each location to address this problem. We modify a baseline GANs architecture by replacing normal convolutions with adaptive convolutions in the generator. Experiments on CIFAR-10 dataset show that our modified models improve the baseline model by a large margin. Furthermore, our models achieve state-of-the-art performance on CIFAR-10 and STL-10 datasets in the unsupervised setting.
A Bayes-Optimal View on Adversarial Examples
Authors: Eitan Richardson, Yair Weiss

Adversarial attacks on CNN classifiers can make an imperceptible change to an input image and alter the classification result. The source of these failures is still poorly understood, and many explanations invoke the "unreasonably linear extrapolation" used by CNNs along with the geometry of high dimensions.
In this paper we show that similar attacks can be used against the Bayes-Optimal classifier for certain class distributions, while for others the optimal classifier is robust to such attacks. We present analytical results showing conditions on the data distribution under which all points can be made arbitrarily close to the optimal decision boundary and show that this can happen even when the classes are easy to separate, when the ideal classifier has a smooth decision surface and when the data lies in low dimensions. We introduce new datasets of realistic images of faces and digits where the Bayes-Optimal classifier can be calculated efficiently and show that for some of these datasets the optimal classifier is robust and for others it is vulnerable to adversarial examples. In systematic experiments with many such datasets, we find that standard CNN training consistently finds a vulnerable classifier even when the optimal classifier is robust while large-margin methods often find a robust classifier with the exact same training data. Our results suggest that adversarial vulnerability is not an unavoidable consequence of machine learning in high dimensions, and may often be a result of suboptimal training methods used in current practice.
Improving Sampling from Generative Autoencoders with Markov Chains
Authors: Antonia Creswell, Kai Arulkumaran, Anil Anthony Bharath

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent distribution learned by the inference model. We call the distribution to which the inference model maps observed samples, the learned latent distribution, which may not be consistent with the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively decoding and encoding, which allows us to sample from the learned latent distribution. Since, the generative model learns to map from the learned latent distribution, rather than the prior, we may use MCMC to improve the quality of samples drawn from the generative model, especially when the learned latent distribution is far from the prior. Using MCMC sampling, we are able to reveal previously unseen differences between generative autoencoders trained either with or without a denoising criterion.
Range-Net: A High Precision Neural SVD
Authors: Soumyajit Gupta, Gurpreet Singh, Clint N. Dawson

For Big Data applications, computing a rank-$r$ Singular Value Decomposition (SVD) is restrictive due to the main memory requirements. Recently introduced streaming Randomized SVD schemes work under the restrictive assumption that the singular value spectrum of the data has an exponential decay. This is seldom true for any practical data. Further, the approximation errors in the singular vectors and values are high due to the randomized projection. We present Range-Net as a low memory alternative to rank-$r$ SVD that satisfies the lower bound on tail-energy given by Eckart-Young-Mirsky (EYM) theorem at machine precision. Range-Net is a deterministic two-stage neural optimization approach with random initialization, where the memory requirement depends explicitly on the feature dimension and desired rank, independent of the sample dimension. The data samples are read in a streaming manner with the network minimization problem converging to the desired rank-$r$ approximation. Range-Net is fully interpretable where all the network outputs and weights have a specific meaning. We provide theoretical guarantees that Range-Net extracted SVD factors satisfy EYM tail-energy lower bound with numerical experiments on real datasets at various scales that confirm these bounds. A comparison against the state-of-the-art streaming Randomized SVD shows that Range-Net is six orders of magnitude more accurate in terms of tail energy while correctly extracting the singular values and vectors.