nils-herrmann/clean_scirepeval huggingface dataset:
GRODIN: Improved Large-Scale Out-of-Domain detection via Back-propagation
Authors: Gleb Yengalych, Igor E. Kuralenok, Vasily A Ershov

Uncertainty estimation and out-of-doman (OOD) input detection are critical for improving the safety and robustness of machine learning. Unfortunately, most methods for detecting OOD examples have been evaluated on small tasks while typical methods are computationally expensive. In this paper we propose a new gradient-based method called GRODIN for OOD detection. The proposed method is conceptually simple, computationally cheaper than ensemble methods and can be directly applied to any existing and deployed model without re-training. We evaluate GRODIN on models trained on CIFAR-10 and ImageNet datasets, and show it's strong performance on various OOD ImageNet datasets such as ImageNet-O, ImageNet-A, ImageNet-R, ImageNet-C.
On Anytime Learning at Macroscale
Authors: Lucas Caccia, Jing Xu, Myle Ott, MarcAurelio Ranzato, Ludovic Denoyer

Classical machine learning frameworks assume access to a possibly large dataset in order to train a predictive model. In many practical applications however, data does not arrive all at once, but in batches over time. This creates a natural trade-off between accuracy of a model and time to obtain such a model. A greedy predictor could produce non-trivial predictions by immediately training on batches as soon as these become available but, it may also make sub-optimal use of future data. On the other hand, a tardy predictor could wait for a long time to aggregate several batches into a larger dataset, but ultimately deliver a much better performance.  In this work, we consider such a streaming learning setting,  which we dub {\em anytime learning at macroscale} (ALMA). It is an instance of anytime learning applied not at the level of a single chunk of data, but at the level of the entire  sequence of large batches. We first formalize this learning setting, we then introduce metrics to assess how well learners perform on the given task for a given memory and compute budget, and finally we test about thirty baseline approaches on three standard benchmarks repurposed for anytime learning at macroscale. Our findings indicate that no model strikes the best trade-off across the board. While replay-based methods attain the lowest error rate, they also incur in a 5 to 10 times increase of compute. Approaches that grow capacity over time do offer better scaling in terms of training flops, but they also underperform simpler ensembling methods in terms of error rate. Overall, ALMA offers both a good abstraction of the typical learning setting faced everyday by practitioners, and a set of unsolved modeling problems for those interested in efficient learning of dynamic models.
Image Transformer
Authors: Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, Noam Shazeer, Lukasz Kaiser

Image generation has been successfully cast as an autoregressive sequence generation
or transformation problem. Recent work has shown that self-attention is
an effective way of modeling textual sequences. In this work, we generalize a
recently proposed model architecture based on self-attention, the Transformer, to
a sequence modeling formulation of image generation with a tractable likelihood.
By restricting the self-attention mechanism to attend to local neighborhoods we
significantly increase the size of images the model can process in practice, despite
maintaining significantly larger receptive fields per layer than typical convolutional
neural networks. We propose another extension of self-attention allowing it
to efficiently take advantage of the two-dimensional nature of images.
While conceptually simple, our generative models trained on two image data sets
are competitive with or significantly outperform the current state of the art in autoregressive
image generation on two different data sets, CIFAR-10 and ImageNet.
We also present results on image super-resolution with a large magnification ratio,
applying an encoder-decoder configuration of our architecture. In a human
evaluation study, we show that our super-resolution models improve significantly
over previously published autoregressive super-resolution models. Images they
generate fool human observers three times more often than the previous state of
the art.
Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration
Authors: Evan Zheran Liu, Kelvin Guu, Panupong Pasupat, Tianlin Shi, Percy Liang

Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates. This has been a notable problem in training deep RL agents to perform web-based tasks, such as booking flights or replying to emails, where a single mistake can ruin the entire sequence of actions. A common remedy is to "warm-start" the agent by pre-training it to mimic expert demonstrations, but this is prone to overfitting. Instead, we propose to constrain exploration using demonstrations. From each demonstration, we induce high-level "workflows" which constrain the allowable actions at each time step to be similar to those in the demonstration (e.g., "Step 1: click on a textbox; Step 2: enter some text"). Our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows. Workflows prune out bad exploration directions and accelerate the agent’s ability to discover rewards. We use our approach to train a novel neural policy designed to handle the semi-structured nature of websites, and evaluate on a suite of web tasks, including the recent World of Bits benchmark. We achieve new state-of-the-art results, and show that workflow-guided exploration improves sample efficiency over behavioral cloning by more than 100x.
No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networks
Authors: Shyamgopal Karthik, Ameya Prabhu, Puneet K. Dokania, Vineet Gandhi

There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors. The idea is to exploit the label hierarchy (e.g., the WordNet ontology) and consider graph distances as a proxy for mistake severity. Surprisingly, on examining mistake-severity distributions of the top-1 prediction, we find that current state-of-the-art hierarchy-aware deep classifiers do not always show practical improvement over the standard cross-entropy baseline in making better mistakes. The reason for the reduction in average mistake-severity can be attributed to the increase in low-severity mistakes, which may also explain the noticeable drop in their accuracy. To this end, we use the classical Conditional Risk Minimization (CRM) framework for hierarchy-aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra hyperparameters and requires adding just a few lines of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top-$k$ predictions across datasets, with very little loss in accuracy. CRM, because of its simplicity, can be used with any off-the-shelf trained model that provides reliable likelihood estimates.
Stochastic Adversarial Video Prediction
Authors: Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine

Being able to predict what may happen in the future requires an in-depth understanding of the physical and causal rules that govern the world. A model that is able to do so has a number of appealing applications, from robotic planning to representation learning. However, learning to predict raw future observations, such as frames in a video, is exceedingly challenging—the ambiguous nature of the problem can cause a naively designed model to average together possible futures into a single, blurry prediction. Recently, this has been addressed by two distinct approaches: (a) latent variational variable models that explicitly model underlying stochasticity and (b) adversarially-trained models that aim to produce naturalistic images. However, a standard latent variable model can struggle to produce realistic results, and a standard adversarially-trained model underutilizes latent variables and fails to produce diverse predictions. We show that these distinct methods are in fact complementary. Combining the two produces predictions that look more realistic to human raters and better cover the range of possible futures. Our method outperforms prior works in these aspects.
LightPAFF: A Two-Stage Distillation Framework for Pre-training and Fine-tuning
Authors: Kaitao Song, Hao Sun, Xu Tan, Tao Qin, Jianfeng Lu, Hongzhi Liu, Tie-Yan Liu

While pre-training and fine-tuning, e.g., BERT~\citep{devlin2018bert}, GPT-2~\citep{radford2019language}, have achieved great success in language understanding and generation tasks, the pre-trained models are usually too big for online deployment in terms of both memory cost and inference speed, which hinders them from practical online usage. In this paper, we propose LightPAFF, a Lightweight Pre-training And Fine-tuning Framework that leverages two-stage knowledge distillation to transfer knowledge from a big teacher model to a lightweight student model in both pre-training and fine-tuning stages. In this way the lightweight model can achieve similar accuracy as the big teacher model, but with much fewer parameters and thus faster online inference speed. LightPAFF can support different pre-training methods (such as BERT, GPT-2 and MASS~\citep{song2019mass}) and be applied to many downstream tasks. Experiments on three language understanding tasks, three language modeling tasks and three sequence to sequence generation tasks demonstrate that while achieving similar accuracy with the big BERT, GPT-2 and MASS models, LightPAFF reduces the model size by nearly 5x and improves online inference speed by 5x-7x.
Flow++: Improving Flow-Based Generative Models  with  Variational Dequantization and Architecture Design  
Authors: Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel

Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. 
Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models.
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
Authors: Prithviraj Ammanabrolu, Matthew Hausknecht

Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in combinatorially-large text-based action spaces. We present KG-A2C, an agent that builds a dynamic knowledge graph while exploring and generates actions using a template-based action space. We contend that the dual uses of the knowledge graph to reason about game state and to constrain natural language generation are the keys to scalable exploration of combinatorially large natural language actions. Results across a wide variety of IF games show that KG-A2C outperforms current IF agents despite the exponential increase in action space size.
On Non-Random Missing Labels in Semi-Supervised Learning
Authors: Xinting Hu, Yulei Niu, Chunyan Miao, Xian-Sheng Hua, Hanwang Zhang

Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the widely-adopted yet naive Missing Completely At Random assumption where both labeled and unlabeled data share the same class distribution. Different from existing SSL solutions that overlook the role of  ''class'' in causing the non-randomness, e.g., users are more likely to label popular classes, we explicitly incorporate ''class'' into SSL. Our method is three-fold: 1) We propose Class-Aware Propensity (CAP) that exploits the unlabeled data to train an improved classifier using the biased labeled data. 2) To encourage rare class training, whose model is low-recall but high-precision that discards too many pseudo-labeled data, we propose Class-Aware Imputation (CAI) that dynamically decreases (or increases) the pseudo-label assignment threshold for rare (or frequent) classes. 3) Overall, we integrate CAP and CAI into a Class-Aware Doubly Robust (CADR) estimator for training an unbiased SSL model. Under various MNAR settings and ablations, our method not only significantly outperforms existing baselines, but also surpasses other label bias removal SSL methods.
Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
Authors: Chris Cannella, Mohammadreza Soltani, Vahid Tarokh

We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the exact conditional distributions learned by normalizing flows. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data. Through experimental tests applying normalizing flows to missing data tasks for a variety of data sets, we demonstrate the efficacy of PL-MCMC for conditional sampling from normalizing flows.
Neural Network Regression with Beta, Dirichlet, and Dirichlet-Multinomial Outputs
Authors: Peter Sadowski, Pierre Baldi

We propose a method for quantifying uncertainty in neural network regression models when the targets are real values on a $d$-dimensional simplex, such as probabilities. We show that each target can be modeled as a sample from a Dirichlet distribution, where the parameters of the Dirichlet are provided by the output of a neural network, and that the combined model can be trained using the gradient of the data likelihood. This approach provides interpretable predictions in the form of multidimensional distributions, rather than point estimates, from which one can obtain confidence intervals or quantify risk in decision making. Furthermore, we show that the same approach can be used to model targets in the form of empirical counts as samples from the Dirichlet-multinomial compound distribution. In experiments, we verify that our approach provides these benefits without harming the performance of the point estimate predictions on two diverse applications: (1) distilling deep convolutional networks trained on CIFAR-100, and (2) predicting the location of particle collisions in the XENON1T Dark Matter detector.
Generalized Kernel Thinning
Authors: Raaz Dwivedi, Lester Mackey

The kernel thinning (KT) algorithm of Dwivedi and Mackey (2021) compresses a probability distribution more effectively than independent sampling by targeting a reproducing kernel Hilbert space (RKHS) and leveraging a less smooth square-root kernel. Here we provide four improvements.  First, we show that KT applied directly to the target RKHS yields tighter, dimension-free guarantees for any kernel, any distribution, and any fixed function in the RKHS. Second, we show that, for analytic kernels like Gaussian, inverse multiquadric, and sinc, target KT admits maximum mean discrepancy (MMD) guarantees comparable to or better than those of square-root KT without making explicit use of a square-root kernel.  Third, we prove that KT with a fractional power kernel yields better-than-Monte-Carlo MMD guarantees for non-smooth kernels, like Laplace and Matern, that do not have square-roots. Fourth, we establish that KT applied to a sum of the target and power kernels (a procedure we call KT+) simultaneously inherits the improved MMD guarantees of power KT and the tighter individual function guarantees of target KT.  In our experiments with target KT and KT+, we witness significant improvements in integration error even in 100 dimensions and when compressing challenging differential equation posteriors.
Axial Attention in Multidimensional Transformers
Authors: Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, Tim Salimans

Self-attention effectively captures large receptive fields with high information bandwidth, but its computational resource requirements grow quadratically with the number of points over which attention is performed. For data arranged as large multidimensional tensors, such as images and videos, the quadratic growth makes self-attention prohibitively expensive. These tensors often have thousands of positions that one wishes to capture and proposed attentional alternatives either limit the resulting receptive field or require custom subroutines. We propose Axial Attention, a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. The Axial Transformer uses axial self-attention layers and a shift operation to efficiently build large and full receptive fields.  Notably the proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. This semi-parallel structure goes a long way to making decoding from even a very large Axial Transformer broadly applicable. We demonstrate state-of-the-art results for the Axial Transformer on the ImageNet-32 and ImageNet-64 image benchmarks as well as on the BAIR Robotic Pushing video benchmark. We open source the implementation of Axial Transformers.
Evo-NAS: Evolutionary-Neural Hybrid Agent for Architecture Search
Authors: Krzysztof Maziarz, Mingxing Tan, Andrey Khorlin, Kuang-Yu Samuel Chang, Andrea Gesmundo

Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is critical for such a resource intensive application. In order to capture the best of both worlds, we propose a class of Evolutionary-Neural hybrid agents (Evo-NAS). We show that the Evo-NAS agent outperforms both neural and evolutionary agents when applied to architecture search for a suite of text and image classification benchmarks. On a high-complexity architecture search space for image classification, the Evo-NAS agent surpasses the accuracy achieved by commonly used agents with only 1/3 of the search cost.
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Authors: Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut

Increasing model size when pretraining natural language representations often results in improved performance on downstream tasks. However, at some point further model increases become harder due to GPU/TPU memory limitations and longer training times. To address these problems,  we present two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT~\citep{devlin2018bert}. Comprehensive empirical evidence shows that our proposed methods lead to models that scale much better compared to the original BERT. We also use a self-supervised loss that focuses on modeling inter-sentence coherence, and show it consistently helps downstream tasks with multi-sentence inputs. As a result, our best model establishes new state-of-the-art results on the GLUE, RACE, and \squad benchmarks while having fewer parameters compared to BERT-large. The code and the pretrained models are available at https://github.com/google-research/ALBERT.
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
Authors: Xiaojun Xu, Jacky Y. Zhang, Evelyn Ma, Danny Son, Oluwasanmi O Koyejo, Bo Li

Machine learning (ML) robustness and generalization are fundamentally correlated: they essentially concern about data distribution shift under adversarial and natural settings, respectively. Thus, it is critical to uncover their underlying connections to tackle one based on the other. On the one hand, recent studies show that more robust (adversarially trained) models are more generalizable to other domains. On the other hand, there lacks of theoretical understanding of such phenomenon and it is not clear whether there are counterexamples. In this paper, we aim to provide sufficient conditions for this phenomenon considering different factors that could affect both, such as the norm of last layer norm, Jacobian norm, and data augmentations (DA). In particular, we propose a general theoretical framework indicating factors that can be reformed as a function class regularization process, which could lead to the improvement of domain generalization. Our analysis, for the first time, shows that ``robustness" is actually not the causation for domain generalization; rather, robustness induced by adversarial training is a by-product of such function class regularization. We then discuss in details about different properties of DA and we prove that under certain conditions, DA can be viewed as regularization and therefore improve generalization. We conduct extensive experiments to verify our theoretical findings, and show several counterexamples where robustness and generalization are negatively correlated when the sufficient conditions are not satisfied.
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies
Authors: Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz

A main theoretical interest in biology and physics is to identify the nonlinear dynamical system (DS) that generated observed time series. Recurrent Neural Networks (RNN) are, in principle, powerful enough to approximate any underlying DS, but in their vanilla form suffer from the exploding vs. vanishing gradients problem. Previous attempts to alleviate this problem resulted either in more complicated, mathematically less tractable RNN architectures, or strongly limited the dynamical expressiveness of the RNN. 
Here we address this issue by suggesting a simple regularization scheme for vanilla RNN with ReLU activation which enables them to solve long-range dependency problems and express slow time scales, while retaining a simple mathematical structure which makes their DS properties partly analytically accessible. We prove two theorems that establish a tight connection between the regularized RNN dynamics and their gradients, illustrate on DS benchmarks that our regularization approach strongly eases the reconstruction of DS which harbor widely differing time scales, and show that our method is also en par with other long-range architectures like LSTMs on several tasks.