nils-herrmann/clean_scirepeval huggingface dataset:
Introducing Sample Robustness
Authors: Monty Maximilian Zühlke

Choosing the right data and model for a pre-defined task is one of the critical competencies in machine learning. Investigating what features of a dataset and its underlying distribution a model decodes may enlighten the mysterious "black box" and guide us to a deeper and more profound understanding of the ongoing processes. Furthermore, it will help to improve the quality of models which directly depend on data or learn from it through training. In this work, we introduce the dataset-dependent concept of sample robustness, which is based on a point-wise Lipschitz constant of the label map. For a particular sample, it measures how small of a perturbation is required to cause a label-change relative to the magnitude of the label map. We introduce theory to motivate the concept and to analyse the effects of having similar robustness distributions for the training- and test data. Afterwards, we conduct various experiments using different datasets and (non-)deterministic models. In some cases, we can boost performance by choosing specifically tailored training(sub)sets and hyperparameters depending on the robustness distribution of the test(sub)sets.
Evaluating Robustness of Cooperative MARL
Authors: Nhan Pham, Lam M. Nguyen, Jie Chen, Thanh Lam Hoang, Subhro Das, Tsui-Wei Weng

In recent years, a proliferation of methods were developed for multi-agent reinforcement learning (MARL). In this paper, we focus on evaluating the robustness of MARL agents in continuous control tasks. In particular, we propose the first model-based approach to perform adversarial attacks for cooperative MARL. We design effective attacks to degrade the MARL agent's performance by adversarially perturbing the states of agent(s) and solving an optimization problem. In addition, we also developed several strategies to select the most vulnerable agents that help to further decrease the team reward of MARL. Extensive numerical experiments on multi-agent Mujoco tasks verify the effectiveness of our proposed approach.
Improving Tail Label Prediction for Extreme Multi-label Learning
Authors: Tong Wei, Wei-Wei Tu, Yu-Feng Li

Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set. Many previous methods treat labels uniformly such that the learned model tends to perform better on head labels, while the performance is severely deteriorated for tail labels. However, it is often desirable to predict more tail labels in many real-world applications. To alleviate this problem, in this work, we show theoretical and experimental evidence for the inferior performance of representative XML methods on tail labels. Our finding is that the norm of label classifier weights typically follows a long-tailed distribution similar to the label frequency, which results in the over-suppression of tail labels. Base on this new finding, we present two new modules: (1)~\algoa~learns to re-rank the predictions by optimizing a population-aware loss, which predicts tail labels with high rank; (2)~\algob~augments tail labels via a decoupled learning scheme, which can yield more balanced classification boundary. We conduct experiments on commonly used XML benchmarks with hundreds of thousands of labels, showing that the proposed methods improve the performance of many state-of-the-art XML models by a considerable margin (6\% performance gain with respect to PSP@1 on average).
Noisy Agents: Self-supervised Exploration by Predicting Auditory Events
Authors: Chuang Gan, Xiaoyu Chen, Phillip Isola, Antonio Torralba, Joshua B. Tenenbaum

Humans integrate multiple sensory modalities (e.g., visual and audio) to build a causal understanding of the physical world. In this work, we propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions through auditory event prediction. First, we allow the agent to collect a small amount of acoustic data and use K-means to discover underlying auditory event clusters. We then train a neural network to predict the auditory events and use the prediction errors as intrinsic rewards to guide RL exploration.  We first conduct an in-depth analysis of our module using a set of Atari games. We then apply our model to audio-visual exploration using the Habitat simulator and active learning using the TDW simulator. Experimental results demonstrate the advantages of using audio signals over vision-based models as intrinsic rewards to guide RL explorations.
Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria
Authors: Boyi Liu, Zhuoran Yang, Zhaoran Wang

Fictitious Self-Play (FSP) has achieved significant empirical success in solving extensive-form games. 
However, from a theoretical perspective, it remains unknown whether FSP is guaranteed to converge to Nash equilibria in Markov games.
As an initial attempt, we propose an FSP algorithm for two-player zero-sum Markov games, dubbed as smooth FSP, where both agents adopt an entropy-regularized policy optimization method against each other. 
Smooth FSP builds upon a connection between smooth fictitious play and the policy optimization framework. Specifically, in each iteration, each player infers the policy of the opponent implicitly via policy evaluation and improves its current policy by taking the smoothed best-response via a proximal policy optimization (PPO) step. 
Moreover, to tame the non-stationarity caused by the opponent, we propose to incorporate entropy regularization in PPO for algorithmic stability. 
When both players adopt smooth FSP simultaneously, i.e., with self-play, we prove that the sequence of joint policies converges to a neighborhood of a Nash equilibrium at a sublinear $\tilde{O}(1/T)$ rate, where $T$ is the number of iterations. To our best knowledge, we establish the first finite-time convergence guarantee for FSP-type algorithms in zero-sum Markov games.
A Solution to China Competitive Poker Using Deep Learning
Authors: Zhenxing Liu, Maoyu Hu, Zhangfei Zhang

Recently, deep neural networks have achieved superhuman performance in various games such as Go, chess and Shogi. Compared to Go, China Competitive Poker, also known as Dou dizhu, is a type of imperfect information game, including hidden information, randomness, multi-agent cooperation and competition. It has become widespread and is now a national game in China. We introduce an approach to play China Competitive Poker using Convolutional Neural Network (CNN) to predict actions. This network is trained by supervised learning from human game records. Without any search, the network already beats the best AI program by a large margin, and also beats the best human amateur players in duplicate mode.
Translation Between Waves,  wave2wave
Authors: Tsuyoshi Okita, Hirotaka Hachiya, Sozo Inoue, Naonori Ueda

The understanding of sensor data has been greatly improved by advanced deep learning methods with big data. However, available sensor data in the real world are still limited, which is called the opportunistic sensor problem.  This paper proposes a new variant of neural machine translation seq2seq to deal with continuous signal waves by introducing the window-based (inverse-) representation to adaptively represent partial shapes of waves and the iterative back-translation model for high-dimensional data.  Experimental results are shown for two real-life data: earthquake and activity translation.  The performance improvements of one-dimensional data was about 46 % in test loss and that of high-dimensional data was about 1625 % in perplexity with regard to the original seq2seq.
GraphGAN: Generating Graphs via Random Walks
Authors: Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

We propose GraphGAN - the first implicit generative model for graphs that enables to mimic real-world networks.
We pose the problem of graph generation as learning the distribution of biased random walks over a single input graph.
Our model is based on a stochastic neural network that generates discrete output samples, and is trained using the Wasserstein GAN objective. GraphGAN enables us to generate sibling graphs, which have similar properties yet are not exact replicas of the original graph. Moreover, GraphGAN learns a semantic mapping from the latent input space to the generated graph's properties. We discover that sampling from certain regions of the latent space leads to varying properties of the output graphs, with smooth transitions between them. Strong generalization properties of GraphGAN are highlighted by its competitive performance in link prediction as well as promising results on node classification, even though not specifically trained for these tasks.
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series
Authors: Yunfei Chu, Xiaowei Wang, Chunyan Feng, Jianxin Ma, Jingren Zhou, Hongxia Yang

Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data. In many real-world systems, it is common to encounter a large amount of multivariate time series data collected from heterogeneous individuals with sharing commonalities, however there are ongoing concerns regarding its applicability in such large scale complex scenarios, presenting both challenges and opportunities for Granger causal reconstruction. To bridge this gap,  we propose a Granger cAusal StructurE Reconstruction (GASER) framework for inductive Granger causality learning and common causal structure detection on heterogeneous multivariate time series. In particular, we address the problem through a novel attention mechanism, called prototypical Granger causal attention. Extensive experiments, as well as an online A/B test on an E-commercial advertising platform, demonstrate the superior performances of GASER.
Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks
Authors: Soufiane Hayou, Arnaud Doucet, Judith Rousseau

Recent work by Jacot et al. (2018) has showed that training a neural network of any kind with gradient descent in parameter space is equivalent to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK). Lee et al. (2019) built on this result to show that the output of a neural network trained using full batch gradient descent can be approximated by a linear model for wide networks. In parallel, a recent line of studies ( Schoenhols et al. (2017), Hayou et al. (2019)) suggested that a special initialization known as the Edge of Chaos leads to good performance. In this paper, we bridge the gap between this two concepts and show the impact of the initialization and the activation function on the NTK as the network depth becomes large. We provide experiments illustrating our theoretical results.
Non-Denoising Forward-Time Diffusions
Authors: Stefano Peluchetti

The scope of this paper is generative modeling through diffusion processes.
An approach falling within this paradigm is the work of Song et al. (2021), which relies on a time-reversal argument to construct a diffusion process targeting the desired data distribution.
We show that the time-reversal argument, common to all denoising diffusion probabilistic modeling proposals, is not necessary.
We obtain diffusion processes targeting the desired data distribution by taking appropriate mixtures of diffusion bridges.
The resulting transport is exact by construction, allows for greater flexibility in choosing the dynamics of the underlying diffusion, and can be approximated by means of a neural network via novel training objectives.
We develop an unifying view of the drift adjustments corresponding to our and to time-reversal approaches and make use of this representation to inspect the inner workings of diffusion-based generative models.
Finally, we leverage on scalable simulation and inference techniques common in spatial statistics to move beyond fully factorial distributions in the underlying diffusion dynamics.
The methodological advances contained in this work contribute toward establishing a general framework for generative modeling based on diffusion processes.
Unsupervised Object Learning via Common Fate
Authors: Matthias Tangemann, Steffen Schneider, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Thomas Brox, Matthias Kuemmerer, Matthias Bethge, Bernhard Schölkopf

Learning generative object models from unlabelled videos is a long standing problem and is required for causal scene modeling. We decompose this problem into three easier subtasks, and provide candidate solutions for each of them. Inspired by the Common Fate Principle of Gestalt Psychology, we first extract (noisy) masks of moving objects via unsupervised motion segmentation. Second, generative models are trained on the masks of the background and the moving objects, respectively. Third, background and foreground models are combined in a conditional ``dead leaves scene model to sample novel scene configurations where occlusions and depth layering  arise naturally. To evaluate the individual stages, we introduce the Fishbowl dataset positioned between complex real-world scenes and common object-centric benchmarks of simplistic objects. We show that our approach allows learning generative models that generalize beyond the occlusions present in the input videos, and represent scenes in a modular fashion that allows sampling plausible scenes outside the training distribution by permitting, for instance, object numbers or densities not observed in the training set.
Enforcing constraints on outputs with unconstrained inference
Authors: Jay Yoon Lee, Michael L. Wick, Jean-Baptiste Tristan

Increasingly, practitioners apply neural networks to complex
  problems in natural language processing (NLP), such as syntactic
  parsing, that have rich output structures. Many such applications
  require deterministic constraints on the output values; for example,
  requiring that the sequential outputs encode a valid tree. While
  hidden units might capture such properties, the network is not
  always able to learn them from the training data alone, and
  practitioners must then resort to post-processing. In this paper, we
  present an inference method for neural networks that enforces
  deterministic constraints on outputs without performing
  post-processing or expensive discrete search over the feasible
  space. Instead, for each input, we nudge the continuous weights
  until the network's unconstrained inference procedure generates an
  output that satisfies the constraints. We find that our method
  reduces the number of violating outputs by up to 81\%, while
  improving accuracy.
Variational Wasserstein gradient flow
Authors: Jiaojiao Fan, Amirhossein Taghvaei, Yongxin Chen

The gradient flow of a function over the space of probability densities with respect to the Wasserstein metric often exhibits nice properties and has been utilized in several machine learning applications. The standard approach to compute the Wasserstein gradient flow is the finite difference which discretizes the underlying space over a grid, and is not scalable. In this work, we propose a scalable proximal gradient type algorithm for Wasserstein gradient flow. The key of our method is a variational formulation of the objective function, which makes it possible to realize the JKO proximal map through a primal-dual optimization. This primal-dual problem can be efficiently solved by alternatively updating the parameters in the inner and outer loops. Our framework covers all the classical Wasserstein gradient flows including the heat equation and the porous medium equation. We demonstrate the performance and scalability of our algorithm with several numerical examples.
Predicted Variables in Programming
Authors: Victor Carbune, Thierry Coppey, Alexander Daryin, Thomas Deselaers, Nikhil Sarda, Jay Yagnik

We present Predicted Variables, an approach to making machine learning (ML) a first class citizen in programming languages.
There is a growing divide in approaches to building systems: using human experts (e.g. programming) on the one hand, and using behavior learned from data (e.g. ML) on the other hand. PVars aim to make using ML in programming easier by hybridizing the two. We leverage the existing concept of variables and create a new type, a predicted variable. PVars are akin to native variables with one important distinction: PVars determine their value using ML when evaluated. We describe PVars and their interface, how they can be used in programming, and demonstrate the feasibility of our approach on three algorithmic problems: binary search, QuickSort, and caches.
We show experimentally that PVars are able to improve over the commonly used heuristics and lead to a better performance than the original algorithms.
As opposed to previous work applying ML to algorithmic problems, PVars have the advantage that they can be used within the existing frameworks and do not require the existing domain knowledge to be replaced. PVars allow for a seamless integration of ML into existing systems and algorithms.
Our PVars implementation currently relies on standard Reinforcement Learning (RL) methods. To learn faster, PVars use the heuristic function, which they are replacing, as an initial function. We show that PVars quickly pick up the behavior of the initial function and then improve performance beyond that without ever performing substantially worse -- allowing for a safe deployment in critical applications.
Don't stack layers in graph neural networks, wire them randomly
Authors: Diego Valsesia, Giulia Fracastoro, Enrico Magli

Graph neural networks have become a staple in problems addressing learning and analysis of data defined over graphs. However, several results suggest an inherent difficulty in extracting better performance by increasing the number of layers. Besides the classic vanishing gradient issues, recent works attribute this to a phenomenon peculiar to the extraction of node features in graph-based tasks, i.e., the need to consider multiple neighborhood sizes at the same time and adaptively tune them. In this paper, we investigate the recently proposed randomly wired architectures in the context of graph neural networks. Instead of building deeper networks by stacking many layers, we prove that employing a randomly-wired architecture can be a more effective way to increase the capacity of the network and obtain richer representations. We show that such architectures behave like an ensemble of paths, which are able to merge contributions from receptive fields of varied size. Moreover, these receptive fields can also be modulated to be wider or narrower through the trainable weights over the paths. We also provide extensive experimental evidence of the superior performance of randomly wired architectures over three tasks and five graph convolution definitions, using a recent benchmarking framework that addresses the reliability of previous testing methodologies.
Benefits of Depth for Long-Term Memory of Recurrent Networks
Authors: Yoav Levine, Or Sharir, Amnon Shashua

The key attribute that drives the unprecedented success of modern Recurrent Neural Networks (RNNs) on learning tasks which involve sequential data, is their ever-improving ability to model intricate long-term temporal dependencies. However, a well established measure of RNNs' long-term memory capacity is lacking, and thus formal understanding of their ability to correlate data throughout time is limited. Though depth efficiency in convolutional networks is well established by now, it does not suffice in order to account for the success of deep RNNs on inputs of varying lengths, and the need to address their 'time-series expressive power' arises. In this paper, we analyze the effect of depth on the ability of recurrent networks to express correlations ranging over long time-scales. To meet the above need, we introduce a measure of the information flow across time that can be supported by the network, referred to as the Start-End separation rank. Essentially, this measure reflects the distance of the function realized by the recurrent network from a function that models no interaction whatsoever between the beginning and end of the input sequence. We prove that deep recurrent networks support Start-End separation ranks which are exponentially higher than those supported by their shallow counterparts. Moreover, we show that the ability of deep recurrent networks to correlate different parts of the input sequence increases exponentially as the input sequence extends, while that of vanilla shallow recurrent networks does not adapt to the sequence length at all. Thus, we establish that depth brings forth an overwhelming advantage in the ability of recurrent networks to model long-term dependencies, and provide an exemplar of quantifying this key attribute which may be readily extended to other RNN architectures of interest, e.g. variants of LSTM networks. We obtain our results by considering a class of recurrent networks referred to as Recurrent Arithmetic Circuits (RACs), which merge the hidden state with the input via the Multiplicative Integration operation.
Accurate and fast detection of copy number variations from short-read whole-genome sequencing with deep convolutional neural network
Authors: Jiajin Li, Stephen Hwang, Luke Zhang, Jae Hoon Sul

A copy number variant (CNV) is a type of genetic mutation where a stretch of DNA is lost or duplicated once or multiple times. CNVs play important roles in the development of diseases and complex traits. CNV detection with short-read DNA sequencing technology is challenging because CNVs significantly vary in size and are similar to DNA sequencing artifacts. Many methods have been developed but still yield unsatisfactory results with high computational costs. Here, we propose CNV-Net, a novel approach for CNV detection using a six-layer convolutional neural network. We encode DNA sequencing information into RGB images and train the convolutional neural network with these images. The fitted convolutional neural network can then be used to predict CNVs from DNA sequencing data. We benchmark CNV-Net with two high-quality whole-genome sequencing datasets available from the Genome in a Bottle Consortium, considered as gold standard benchmarking datasets for CNV detection. We demonstrate that CNV-Net is more accurate and efficient in CNV detection than current tools.
How Important is Importance Sampling for Deep Budgeted Training?
Authors: Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, Kevin McGuinness

Long iterative training processes for Deep Neural Networks (DNNs) are commonly required to achieve state-of-the-art performance in many computer vision tasks. Core-set selection and importance sampling approaches might play a key role in budgeted training regimes, i.e.~when limiting the number of training iterations. The former demonstrate that retaining informative samples is important to avoid large drops in accuracy, and the later aim at dynamically estimating the sample importance to speed-up convergence. This work explores this paradigm and how a budget constraint interacts with importance sampling approaches and data augmentation techniques. We show that under budget restrictions, importance sampling approaches do not provide a consistent improvement over uniform sampling. We suggest that, given a specific budget, the best course of action is to disregard the importance and introduce adequate data augmentation. For example, training in CIFAR-10/100 with 30% of the full training budget, a uniform sampling strategy with certain data augmentation surpasses the performance of 100% budget models trained with standard data augmentation. We conclude from our work that DNNs under budget restrictions benefit greatly from variety in the samples and that finding the right samples to train is not the most effective strategy when balancing high performance with low computational requirements. The code will be released after the review process.