nils-herrmann/clean_scirepeval huggingface dataset:
Distribution Embedding Network for Meta-Learning with Variable-Length Input
Authors: Lang Liu, Mahdi Milani Fard, Sen Zhao

We propose Distribution Embedding Network (DEN) for meta-learning, which is designed for applications where both the distribution and the number of features could vary across tasks. DEN first transforms features using a learned piecewise linear function, then learns an embedding of the underlying data distribution after the transformation, and finally classifies examples based on the distribution embedding. We show that the parameters of the distribution embedding and the classification modules can be shared across tasks. We propose a novel methodology to mass-simulate binary classification training tasks, and demonstrate that DEN outperforms existing methods in a number of test tasks in numerical studies.
Composition-based Multi-Relational Graph Convolutional Networks
Authors: Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha Talukdar

Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs. Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it. Most of the existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representations of nodes only. In this paper, we propose CompGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph. CompGCN leverages a variety of entity-relation composition operations from Knowledge Graph Embedding techniques and scales with the number of relations. It also generalizes several of the existing multi-relational GCN methods. We evaluate our proposed method on multiple tasks such as node classification, link prediction, and graph classification, and achieve demonstrably superior results. We make the source code of CompGCN available to foster reproducible research.
Improving Out-of-Distribution Robustness via Selective Augmentation
Authors: Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn

Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shifts is a common problem in real-world applications and can cause models to perform dramatically worse at test time. In this paper, we specifically consider the problems of domain shifts and subpopulation shifts, where learning invariant representations by aligning domain-specific representations or balancing the risks across domains with regularizers are popular solutions. However, designing regularizers that are suitable for diverse real-world datasets is challenging. Instead, we shed new light on addressing distribution shifts by directly eliminating domain-related spurious correlations with augmentation, leading to a simple technique based on mixup, called LISA (Learning Invariant Representations via Selective Augmentation). LISA selectively interpolates samples either with the same labels but different domains or with the same domain but different labels. Empirically, we study the effectiveness of LISA on nine benchmarks ranging from subpopulation shifts to domain shifts. The results indicate that LISA consistently outperforms other state-of-the-art methods with superior invariant representations. The empirical findings are further strengthened by our theoretical analysis.
Batch size-invariance for policy optimization
Authors: Jacob Hilton, Karl Cobbe, John Schulman

We say an algorithm is batch size-invariant if changes to the batch size can largely be compensated for by changes to other hyperparameters. Stochastic gradient descent is well-known to have this property at small batch sizes, via the learning rate. However, some policy optimization algorithms (such as PPO) do not have this property, because of how they control the size of policy updates. In this work we show how to make these algorithms batch size-invariant. Our key insight is to decouple the proximal policy (used for controlling policy updates) from the behavior policy (used for off-policy corrections). Our experiments help explain why these algorithms work, and additionally show how they can make more efficient use of stale data.
Information distance for neural network functions
Authors: Xiao Zhang, Dejing Dou, Ji Wu

We provide a practical distance measure in the space of functions parameterized by neural networks. It is based on the classical information distance, and we propose to replace the uncomputable Kolmogorov complexity with information measured by codelength of prequential coding. We also provide a method for directly estimating the expectation of such codelength with limited examples. Empirically, we show that information distance is invariant with respect to different parameterization of the neural networks. We also verify that information distance can faithfully reflect similarities of neural network functions. Finally, we applied information distance to investigate the relationship between neural network models, and demonstrate the connection between information distance and multiple characteristics and behaviors of neural networks.
Precondition Layer and Its Use for GANs
Authors: Tiantian Fang, Alex Schwing, Ruoyu Sun

One of the major challenges when training generative adversarial nets (GANs) is instability.  To address this instability spectral normalization (SN) is remarkably successful.  However, SN-GAN still suffers from training instabilities, especially when working with higher-dimensional data.  We find that those instabilities are accompanied by large condition numbers of the discriminator weight matrices. To improve training stability we study common linear-algebra practice and employ preconditioning.   Specifically,  we introduce a  preconditioning layer  (PC-layer)that performs a  low-degree polynomial preconditioning.   We use this  PC-layer in two ways:  1)  fixed preconditioning  (FPC)  adds a  fixed  PC-layer to all layers, and 2)  adaptive preconditioning  (APC)  adaptively controls the strength of preconditioning. Empirically, we show that FPC and APC stabilize the training of un-conditional GANs using classical architectures.  On LSUN256×256 data, APC improves FID scores by around 5 points over baselines.
The role of over-parametrization in generalization of neural networks
Authors: Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, Nathan Srebro

Despite existing work on ensuring generalization of neural networks in terms of scale sensitive complexity measures, such as norms, margin and sharpness, these complexity measures do not offer an explanation of why neural networks generalize better with over-parametrization. In this work we suggest a novel complexity measure based on unit-wise capacities resulting in a tighter generalization bound for two layer ReLU networks. Our capacity bound correlates with the behavior of test error with increasing network sizes (within the range reported in the experiments), and could partly explain the improvement in generalization with over-parametrization. We further present a matching lower bound for the Rademacher complexity that improves over previous capacity lower bounds for neural networks.
FLAME-in-NeRF: Neural control of Radiance Fields for Free View Face Animation
Authors: ShahRukh Athar, Zhixin Shu, Dimitris Samaras

This paper presents a neural rendering method for controllable portrait video synthesis.Recent advances in volumetric neural rendering, such as neural radiance fields (NeRF), have enabled the photorealistic novel view synthesis of static scenes with impressive results. However, modeling dynamic and controllable objects as part of a scene with such scene representations is still challenging. 
In this work, we design a system that enables 1) novel view synthesis for portrait video, of both the human subject and the scene they are in and 2) explicit control of the facial expressions through a low-dimensional expression representation. 
We represent the distribution of human facial expressions using the expression parameters of a 3D Morphable Model (3DMMs) and condition the NeRF volumetric function on them. 
Furthermore, we impose a spatial prior, brought by 3DMM fitting,  to guide the network to learn disentangled control for static scene appearance and dynamic facial actions. We show the effectiveness of our method on free view synthesis of portrait videos with expression controls. To train a scene, our method only requires a short video of a subject captured by a mobile device.
Understanding GANs: the LQG Setting
Authors: Soheil Feizi, Changho Suh, Fei Xia, David Tse

Generative Adversarial Networks (GANs) have become a popular method to learn a probability model from data. Many GAN architectures with different optimization metrics have been introduced recently. Instead of proposing yet another architecture, this  paper aims to provide an understanding of some of the basic issues surrounding GANs. First, we propose a natural way of specifying the loss function for GANs by drawing a connection with supervised learning. Second, we shed light on the statistical performance of GANs through the analysis of a simple LQG setting: the generator is linear, the loss function is quadratic and the data is drawn from a Gaussian distribution. We show that   1) the optimal GAN solution converges to population Principal Component Analysis (PCA) as the number of training samples increases; 2) the number of samples required scales exponentially with the dimension of the data; 3) the number of samples scales almost linearly if the discriminator is constrained to be quadratic. Moreover, under this quadratic constraint on the discriminator, the optimal finite-sample GAN performs simply empirical PCA.
H-divergence: A Decision-Theoretic Probability Discrepancy Measure 
Authors: Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon

Measuring the discrepancy between two probability distributions is a fundamental problem in machine learning and statistics. Based on ideas from decision theory, we investigate a new class of discrepancies that are based on the optimal decision loss. Two probability distributions are different if the optimal decision loss is higher on the mixture distribution than on each individual distribution. We show that this generalizes popular notions of discrepancy measurements such as the Jensen Shannon divergence and the maximum mean discrepancy. We apply our approach to two-sample tests, which evaluates whether two sets of samples come from the same distribution. On various benchmark and real datasets, we demonstrate that tests based on our generalized notion of discrepancy is able to achieve superior test power. We also apply our approach to sample quality evaluation as an alternative to the FID score, and to understanding the effects of climate change on different social and economic activities.
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Authors: Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat

Scaling end-to-end reinforcement learning to control real robots from vision presents a series of challenges, in particular in terms of sample efficiency. Against end-to-end learning, state representation learning can help learn a compact, efficient and relevant representation of states that speeds up policy learning, reducing the number of samples needed, and that is easier to interpret. We evaluate several state representation learning methods on goal based robotics tasks and propose a new unsupervised model that stacks representations and combines strengths of several of these approaches. This method encodes all the relevant features, performs on par or better than end-to-end learning, and is robust to hyper-parameters change.
Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions
Authors: Chen Zhu, Zheng Xu, Mingqing Chen, Jakub Konečný, Andrew Hard, Tom Goldstein

Federated learning has been deployed to train machine learning models from decentralized client data on mobile devices in practice. The clients available for training are observed to have periodically shifting distributions changing with the time of day, which can cause instability in training and degrade the model performance. In this paper, instead of modeling the distribution shift with a block-cyclic pattern as previous works, we model it with a mixture of distributions that gradually shifts between daytime and nighttime modes, and find this intuitive model to better match the observations in practical federated learning systems. 
Furthermore, we propose to jointly train a clustering model and a multi-branch network to allocate lightweight specialized branches to clients from different modes. A temporal prior is used to significantly boost the training performance.
Experiments for image classification on EMNIST and CIFAR datasets, and next word prediction on the Stack Overflow dataset show that the proposed algorithm can counter the effects of the distribution shift and significantly improve the final model performance.
Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks
Authors: Xin Jiang*, Kewei Cheng*, Song Jiang*, Yizhou Sun

Despite the impressive success of graph convolutional networks (GCNs) on numerous applications, training on large-scale sparse networks remains challenging. Current algorithms require large memory space for storing GCN outputs as well as all the intermediate embeddings. Besides, most of these algorithms involves either random sampling or an approximation of the adjacency matrix, which might unfortunately lose important structure information. In this paper, we propose Chordal-GCN for semi-supervised node classification. The proposed model utilizes the exact graph structure (i.e., without sampling or approximation), while requires limited memory resources compared with the original GCN. Moreover, it leverages the sparsity pattern as well as the clustering structure of the graph. The proposed model first decomposes a large-scale sparse network into several small dense subgraphs (called cliques), and constructs a clique tree. By traversing the tree, GCN training is performed clique by clique, and connections between cliques are exploited via the tree hierarchy. Furthermore, we implement Chordal-GCN on large-scale datasets and demonstrate superior performance.
Neural SDEs Made Easy: SDEs are Infinite-Dimensional GANs
Authors: Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, Terry Lyons

Several authors have introduced \emph{Neural Stochastic Differential Equations} (Neural SDEs), often involving complex theory with various limitations. Here, we aim to introduce a generic, user friendly approach to neural SDEs. Our central contribution is the observation that an SDE is a map from Wiener measure (Brownian motion) to a solution distribution, which may be sampled from, but which does not admit a straightforward notion of probability density -- and that this is just the familiar formulation of a GAN. This produces a continuous-time generative model, arbitrary drift and diffusions are admissible, and in the infinite data limit any SDE may be learnt. After that, we construct a new scheme for sampling \emph{and reconstructing} Brownian motion, with constant average-case time and memory costs, adapted to the access patterns of an SDE solver. Finally, we demonstrate that the adjoint SDE (used for backpropagation) may be constructed via rough path theory, without the previous theoretical complexity of two-sided filtrations.
Early Stopping by Gradient Disparity
Authors: mahsa forouzesh, Patrick Thiran

Validation-based early-stopping methods are one of the most popular techniques used to avoid over-training deep neural networks. They require to set aside a reliable unbiased validation set, which can be expensive in applications offering limited amounts of data. In this paper, we propose to use \emph{gradient disparity}, which we define as the $\ell_2$ norm distance between the gradient vectors of two batches drawn from the training set. It comes from a probabilistic upper bound on the difference between the classification errors over a given batch, when the network is trained on this batch and when the network is trained on another batch of points sampled from the same dataset. We empirically show that gradient disparity is a very promising early-stopping criterion when data is limited, because it uses all the training samples during training. Furthermore, we show in a wide range of experimental settings that gradient disparity is not only strongly related to the usual generalization error between the training and test sets, but that it is also much more informative about the level of label noise.
Learning To Generate Reviews and Discovering Sentiment
Authors: Alec Radford, Rafal Jozefowicz, Ilya Sutskever

We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment analysis. These representations, learned in an unsupervised manner, achieve state of the art on the binary subset of the Stanford Sentiment Treebank. They are also very data efficient. When using only a handful of labeled examples, our approach matches the performance of strong baselines trained on full datasets. We also demonstrate the sentiment unit has a direct influence on the generative process of the model. Simply fixing its value to be positive or negative generates samples with the corresponding positive or negative sentiment.
Self-supervised Learning from a Multi-view Perspective
Authors: Yao-Hung Hubert Tsai, Yue Wu, Ruslan Salakhutdinov, Louis-Philippe Morency

As a subset of unsupervised representation learning, self-supervised representation learning adopts self-defined signals as supervision and uses the learned representation for downstream tasks, such as object detection and image captioning. Many proposed approaches for self-supervised learning follow naturally a multi-view perspective, where the input (e.g., original images) and the self-supervised signals (e.g., augmented images) can be seen as two redundant views of the data. Building from this multi-view perspective, this paper provides an information-theoretical framework to better understand the properties that encourage successful self-supervised learning. Specifically, we demonstrate that self-supervised learned representations can extract task-relevant information and discard task-irrelevant information. Our theoretical framework paves the way to a larger space of self-supervised learning objective design. In particular, we propose a composite objective that bridges the gap between prior contrastive and predictive learning objectives, and introduce an additional objective term to discard task-irrelevant information. To verify our analysis, we conduct controlled experiments to evaluate the impact of the composite objectives. We also explore our framework's empirical generalization beyond the multi-view perspective, where the cross-view redundancy may not be clearly observed.
Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields
Authors: Thomas Unterthiner, Bernhard Nessler, Calvin Seward, Günter Klambauer, Martin Heusel, Hubert Ramsauer, Sepp Hochreiter

Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model the target distribution. We introduce Coulomb GANs, which pose the GAN learning problem as a potential field, where generated samples are attracted to training set samples but repel each other. The discriminator learns a potential field while the generator decreases the energy by moving its samples along the vector (force) field determined by the gradient of the potential field. Through decreasing the energy, the GAN model learns to generate samples according to the whole target distribution and does not only cover some of its modes. We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution. We show the efficacy of Coulomb GANs on LSUN bedrooms, CelebA faces, CIFAR-10 and the Google Billion Word text generation.
Multiplicative Interactions and Where to Find Them
Authors: Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu

We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.
Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated. We begin by showing that such layers strictly enrich the representable function classes of neural networks. We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required. We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation. Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures.
Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets
Authors: Shixiang Wan, Shikai Luo, Hongtu Zhu

Achieving accurate spatio-temporal predictions in large-scale systems is extremely valuable in many real-world applications, such as weather forecasts, retail forecasting, and urban traffic forecasting. So far, most existing methods for multi-horizon, multi-task and multi-target predictions select important predicting variables via their correlations with responses, and thus it is highly possible that many forecasting models generated from those methods are not causal, leading to poor interpretability. The aim of this paper is to develop a collaborative causal spatio-temporal fusion transformer, named CausalTrans, to establish the collaborative causal effects of predictors on multiple forecasting targets, such as supply and demand in ride-sharing platforms. Specifically, we integrate the causal attention with the Conditional Average Treatment Effect (CATE) estimation method for causal inference. Moreover, we propose a novel and fast multi-head attention evolved from Taylor expansion instead of softmax, reducing time complexity from $O(\mathcal{V}^2)$ to $O(\mathcal{V})$, where $\mathcal{V}$ is the number of nodes in a graph. We further design a spatial graph fusion mechanism to significantly reduce the parameters' scale. We conduct a wide range of experiments to demonstrate the interpretability of causal attention, the effectiveness of various model components, and the time efficiency of our CausalTrans. As shown in these experiments, our CausalTrans framework can achieve up to 15$\%$ error reduction compared with various baseline methods.