nils-herrmann/clean_scirepeval huggingface dataset:
Autoregressive Quantile Flows for Predictive Uncertainty Estimation
Authors: Phillip Si, Allan Bishop, Volodymyr Kuleshov

Numerous applications of machine learning involve representing probability distributions over high-dimensional data. We propose autoregressive quantile flows, a flexible class of normalizing flow models trained using a novel objective based on proper scoring rules. Our objective does not require calculating computationally expensive determinants of Jacobians during training and supports new types of neural architectures, such as neural autoregressive flows from which it is easy to sample. 
    We leverage these models in quantile flow regression, an approach that parameterizes predictive conditional distributions with flows, resulting in improved probabilistic predictions on tasks such as time series forecasting and object detection.
    Our novel objective functions and neural flow parameterizations also yield improvements on popular generation and density estimation tasks, and represent a step beyond maximum likelihood learning of flows.
Jiffy: A Convolutional Approach to Learning Time Series Similarity
Authors: Divya Shanmugam, Davis Blalock, John Guttag

Computing distances between examples is at the core of many learning algorithms for time series. Consequently, a great deal of work has gone into designing effective time series distance measures. We present Jiffy, a simple and scalable distance metric for multivariate time series. Our approach is to reframe the task as a representation learning problem---rather than design an elaborate distance function, we use a CNN to learn an embedding such that the Euclidean distance is effective. By aggressively max-pooling and downsampling, we are able to construct this embedding using a highly compact neural network. Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods.
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models
Authors: Rares-Darius Buhai, Andrej Risteski, Yoni Halpern, David Sontag

One of the most surprising and exciting discoveries in supervising learning was the benefit of overparameterization (i.e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i.e. generalization). In contrast, unsupervised settings have been under-explored, despite the fact that it has been observed that overparameterization can be helpful as early as Dasgupta & Schulman (2007). In this paper, we perform an exhaustive study of different aspects of overparameterization in unsupervised learning via synthetic and semi-synthetic experiments. We discuss benefits to different metrics of success (recovering the parameters of the ground-truth model, held-out log-likelihood), sensitivity to variations of the training algorithm, and behavior as the amount of overparameterization increases. We find that, when learning using  methods such as variational inference,  larger models can significantly increase the number of ground truth latent variables recovered.
Towards Building A Group-based Unsupervised Representation Disentanglement Framework
Authors: Tao Yang, Xuanchi Ren, Yuwang Wang, Wenjun Zeng, Nanning Zheng

Disentangled representation learning is one of the major goals of deep learning, and is a key step for achieving explainable and generalizable models. The key idea of the state-of-the-art VAE-based unsupervised representation disentanglement methods is to minimize the total correlation of the joint distribution of the latent variables. However, it has been proved that their goal can not be achieved without introducing other inductive biases. The Group Theory based definition of representation disentanglement mathematically connects the data transformations to the representations using the formalism of group. In this paper, built on the group-based definition and inspired by the \emph{n-th dihedral group}, we first propose a theoretical framework towards achieving unsupervised representation disentanglement. We then propose a model based on existing VAE-based methods to tackle the unsupervised learning problem of the framework. In the theoretical framework, we prove three sufficient conditions on model, group structure, and data respectively in an effort to achieve, in an unsupervised way, disentangled representation per group-based definition. With these conditions, we offer an option, from the perspective of the group-based definition, for the inductive bias that existing VAE-based models lack. Experimentally, we train 1800 models covering the most prominent VAE-based methods on five datasets to verify the effectiveness of our theoretical framework. Compared to the original VAE-based methods, these Groupified VAEs consistently achieve better mean performance with smaller variances.
Double Generative Adversarial Networks for Conditional Independence Testing
Authors: Chengchun Shi, Tianlin Xu, Wicher Pieter Bergsma, Lexin Li

In this article, we consider the problem of high-dimensional conditional independence testing, which is a key building block in statistics and machine learning. We propose a double generative adversarial networks (GAN)-based inference procedure. We first introduce a double GANs framework to learn two generators, and integrate the two generators to construct a doubly-robust test statistic. We next consider multiple generalized covariance measures, and take their maximum as our test statistic. Finally, we obtain the empirical distribution of our test statistic through multiplier bootstrap. We show that our test controls type-I error, while the
power approaches one asymptotically. More importantly, these theoretical guarantees are obtained under much weaker and practically more feasible conditions compared to existing tests. We demonstrate the efficacy of our test through both synthetic and real datasets.
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation
Authors: Yingjie Fei, Zhuoran Yang, Zhaoran Wang

We study risk-sensitive reinforcement learning with the entropic risk measure and function approximation. We consider the finite-horizon episodic MDP setting, and propose a meta algorithm based on value iteration. We then derive two algorithms for linear and general function approximation, namely RSVI.L and RSVI.G, respectively, as special instances of the meta algorithm. We illustrate that the success of RSVI.L depends crucially on carefully designed feature mapping and regularization that adapt to risk sensitivity. In addition, both RSVI.L and RSVI.G maintain risk-sensitive optimism that facilitates efficient exploration. On the analytic side, we provide regret analysis for the algorithms by developing a meta analytic framework, at the core of which is a risk-sensitive optimism condition. We show that any instance of the meta algorithm that satisfies the condition yields a meta regret bound. We further verify the condition for RSVI.L and RSVI.G under respective function approximation settings to obtain concrete regret bounds that scale sublinearly in the number of episodes.
Shift-tolerant Perceptual Similarity Metric
Authors: Abhijay Ghildyal, Feng Liu

Existing perceptual similarity metrics assume an image and its reference are well aligned. As a result, these metrics are often sensitive to a small alignment error that is imperceptible to the human eyes. This paper studies the effect of small misalignment, specifically a small shift between the input and reference image, on existing metrics, and accordingly develops a shift-tolerant similarity metric. This paper builds upon LPIPS, a widely used learned perceptual similarity metric and explores architectural design considerations to make it robust against imperceptible misalignment. Specifically, we study a wide spectrum of neural network elements, such as anti-aliasing filtering, pooling, striding, padding, and skip connection, and discuss their roles in making a robust metric. Based on our studies, we develop a new deep neural network-based perceptual similarity metric. Our experiments show that our metric is tolerant to imperceptible shifts while being consistent with the human similarity judgment.
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning
Authors: Fan Zhou, Zhoufan Zhu, Qi Kuang, Liwen Zhang

Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address. One is how to ensure the validity of the learned quantile function, the other is how to efficiently utilize the distribution information. This paper attempts to provide some new perspectives to encourage the future in-depth studies in these two fields. We first propose a non-decreasing quantile function network (NDQFN) to guarantee the monotonicity of the obtained quantile estimates and then design a general exploration framework called distributional prediction error (DPE) for DRL which utilizes the entire distribution of the quantile function. In this paper, we not only discuss the theoretical necessity of our method but also show the performance gain it achieves in practice by comparing with some competitors on Atari 2600 Games especially in some hard-explored games.
TabNN: A Universal Neural Network Solution for Tabular Data
Authors: Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, Tie-Yan Liu

Neural Network (NN) has achieved state-of-the-art performances in many tasks within image, speech, and text domains. Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN capturing spatial locality and RNN modeling sequential dependency. Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data. Nevertheless, there are many applications with all kinds of tabular data in other domains. Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all. Without careful architecture design based on domain knowledge, it is quite challenging for NN to reach satisfactory performance in these tabular data domains. To fill the gap of NN in tabular data learning, we propose a universal neural network solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically. Specifically, the design of TabNN follows two principles: \emph{to explicitly leverages expressive feature combinations} and \emph{to reduce model complexity}. Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN. Comprehensive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions.
Using a one dimensional parabolic model of the full-batch loss to estimate learning rates during training
Authors: Maximus Mutschler, Kevin Alexander Laube, Andreas Zell

A fundamental challenge in Deep Learning is to find optimal step sizes for stochastic gradient descent automatically. In traditional optimization, line searches are a commonly used method to determine step sizes. One problem in Deep Learning is that finding appropriate step sizes on the full-batch loss is unfeasibly expensive. Therefore, classical line search approaches, designed for losses without inherent noise, are usually not applicable. Recent empirical findings suggest that the full-batch loss behaves locally parabolically in the direction of noisy update step directions. Furthermore, the trend of the optimal update step size changes slowly. By exploiting these findings, this work introduces a line-search method that approximates the full-batch loss with a parabola estimated over several mini-batches. Learning rates are derived from such parabolas during training. In the experiments conducted, our approach mostly outperforms SGD tuned with a piece-wise constant learning rate schedule and other line search approaches for Deep Learning across models, datasets, and batch sizes on validation and test accuracy.
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs
Authors: Sachin Kumar, Yulia Tsvetkov

The Softmax function is used in the final layer of nearly all existing sequence-to-sequence models for language generation. However, it is usually the slowest layer to compute which limits the vocabulary size to a subset of most frequent types; and it has a large memory footprint. We propose a general technique for replacing the softmax layer with a continuous embedding layer. Our primary innovations are a novel probabilistic loss, and a training and inference procedure in which we generate a probability distribution over pre-trained word embeddings, instead of a multinomial distribution over the vocabulary obtained via softmax. We evaluate this new class of sequence-to-sequence models with continuous outputs on the task of neural machine translation. We show that our models obtain upto 2.5x speed-up in training time while performing on par with the state-of-the-art models in terms of translation quality. These models are capable of handling very large vocabularies without compromising on translation quality. They also produce more meaningful errors than in the softmax-based models, as these errors typically lie in a subspace of the vector space of the reference translations.
Understanding l4-based Dictionary Learning: Interpretation, Stability, and Robustness
Authors: Yuexiang Zhai, Hermish Mehta, Zhengyuan Zhou, Yi Ma

Recently, the $\ell^4$-norm maximization has been proposed to solve the sparse dictionary learning (SDL) problem. The simple MSP (matching, stretching, and projection) algorithm proposed by \cite{zhai2019a} has proved surprisingly efficient and effective.  This paper aims to better understand this algorithm from its strong geometric and statistical connections with the classic PCA and ICA, as well as their associated fixed-point style algorithms. Such connections provide a unified way of viewing problems that pursue {\em principal}, , or {\em sparse} components of high-dimensional data. Our studies reveal additional good properties of $\ell^4$-maximization: not only is the MSP algorithm for sparse coding insensitive to small noise, but it is also robust to outliers and resilient to sparse corruptions. We provide statistical justification for such inherently nice properties. To corroborate the theoretical analysis, we also provide extensive and compelling experimental evidence with both synthetic data and real images.
What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?
Authors: Jiedong Hao, Jing Dong, Wei Wang, Tieniu Tan

Previous work has shown that feature maps of deep convolutional neural networks (CNNs)
can be interpreted as feature representation of a particular image region. Features aggregated from
these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in
recent years. The key to the success of such methods is the feature representation. However, the different
factors that impact the effectiveness of features are still not explored thoroughly. There are much less
discussion about the best combination of them.

The main contribution of our paper is the thorough evaluations of the various factors that affect the
discriminative ability of the features extracted from CNNs. Based on the evaluation results, we also identify 
the best choices for different factors and propose a new multi-scale image feature representation method to 
encode the image effectively. Finally, we show that the proposed method generalises well and outperforms 
the state-of-the-art methods on four typical datasets used for visual instance retrieval.
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting?
Authors: Wei Zhang, Mingrui Liu, Yu Feng, Brian Kingsbury, Yuhai Tu

Distributed Deep Learning (DDL) is essential for large-scale Deep Learning (DL) training. Using a sufficiently large batch size is critical to achieving DDL runtime speedup. In a large batch setting, the learning rate must be increased to compensate for the reduced number of parameter updates. However, a large batch size may converge to sharp minima with poor generalization, and a large learning rate may harm convergence. Synchronous Stochastic Gradient Descent (SSGD) is the de facto DDL optimization method. Recently, Decentralized Parallel SGD (DPSGD) has been proven to achieve a similar convergence rate as SGD and to guarantee linear speedup for non-convex optimization problems. While there was anecdotal evidence that DPSGD outperforms SSGD in the large-batch setting, no systematic study has been conducted to explain why this is the case. Based on a detailed analysis of the DPSGD learning dynamics, we find that DPSGD introduces additional landscape-dependent noise, which has two benefits in the large-batch setting: 1) it automatically adjusts the learning rate to improve convergence; 2) it enhances weight space search by escaping local traps (e.g., saddle points) to find flat minima with better generalization. We conduct extensive studies over 12 state-of-the-art DL models/tasks and demonstrate that DPSGD consistently outperforms SSGD in the large batch setting; 
  and DPSGD converges in cases where SSGD diverges for large learning rates. Our findings are consistent across different application domains, Computer  Networks and Long Short-Term Memory Recurrent Neural Networks.
Knowledge Graph Embedding: A Probabilistic Perspective and Generalization Bounds
Authors: Ondrej Kuzelka, Yuyi Wang

We study theoretical properties of embedding methods for knowledge graph completion under the missing completely at random assumption. We prove generalization error bounds for this setting. Even though the missing completely at random setting may seem naive, it is actually how knowledge graph embedding methods are typically benchmarked in the literature. Our results provide, to certain extent, an explanation for why knowledge graph embedding methods work (as much as classical learning theory results provide explanations for classical learning from i.i.d. data).
Analyzing Attention Mechanisms through Lens of Sample Complexity and Loss Landscape
Authors: Bingyuan Liu, Yogesh Balaji, Lingzhou Xue, Martin Renqiang Min

Attention mechanisms have advanced state-of-the-art deep learning models in many machine learning tasks. Despite significant empirical gains, there is a lack of theoretical analyses on their effectiveness. In this paper, we address this problem by studying the sample complexity and loss landscape of attention-based neural networks. Our results show that, under mild assumptions, every local minimum of the attention model has low prediction error, and attention models require lower sample complexity than models without attention. Besides revealing why popular self-attention works, our theoretical results also provide guidelines for designing future attention models. Experiments on various datasets validate our theoretical findings.
Knowledge Transfer via Student-Teacher Collaboration
Authors: Tianxiao Gao, Ruiqin Xiong, Zhenhua Liu, Siwei ma, Feng Wu, Tiejun Huang, Wen Gao

Accompanying with the flourish development in various fields, deep neural networks, however, are still facing with the plight of high computational costs and storage. One way to compress these heavy models is knowledge transfer (KT), in which a light student network is trained through absorbing the knowledge from a powerful teacher network. In this paper, we propose a novel knowledge transfer method which employs a Student-Teacher Collaboration (STC) network during the knowledge transfer process. This is done by connecting the front part of the student network to the back part of the teacher network as the STC network. The back part of the teacher network takes the intermediate representation from the front part of the student network as input to make the prediction. The difference between the prediction from the collaboration network and the output tensor from the teacher network is taken into account of the loss during the train process. Through back propagation, the teacher network provides guidance to the student network in a gradient signal manner. In this way, our method takes advantage of the knowledge from the entire teacher network, who instructs the student network in learning process. Through plentiful experiments, it is proved that our STC method outperforms other KT methods with conventional strategy.
Learning to Infer User Interface Attributes from Images
Authors: Philippe Schlattner, Pavol Bielik, Martin Vechev

We present a new approach that helps developers automate the process of user interface implementation. Concretely, given an input image created by a designer (e.g, using a vector graphics editor), we learn to infer its implementation which when rendered (e.g., on the Android platform), looks visually the same as the input image. To achieve this, we take a black box rendering engine and a set of attributes it supports (e.g., colors, border radius, shadow or text properties), use it to generate a suitable synthetic training dataset, and then train specialized neural models to predict each of the attribute values. To improve pixel-level accuracy, we also use imitation learning to train a neural policy that refines the predicted attribute values by learning to compute the similarity of the original and rendered images in their attribute space, rather than based on the difference of pixel values.
Non-Markovian Predictive Coding For Planning In Latent Space
Authors: Tung Nguyen, Rui Shu, Tuan Pham, Hung Bui, Stefano Ermon

High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. In order to handle high-dimensional sensory inputs, existing MBRL approaches use representation learning to map high-dimensional observations into a lower-dimensional latent space that is more amenable to dynamics estimation and planning. Crucially, the task-relevance and predictability of the learned representations play critical roles in the success of planning in latent space. In this work, we present Non-Markovian Predictive Coding (NMPC), an information-theoretic approach for planning from high-dimensional observations with two key properties: 1) it formulates a mutual information objective that prioritizes the encoding of task-relevant components of the environment; and 2) it employs a recurrent neural network capable of modeling non-Markovian latent dynamics. To demonstrate NMPC’s ability to prioritize task-relevant information, we evaluate our new model on a challenging modification of standard DMControl tasks where the DMControl background is replaced with natural videos, containing complex but irrelevant information to the planning task. Our experiments show that NMPC is superior to existing methods in the challenging complex-background setting while remaining competitive with current state-of-the-art MBRL models in the standard setting.