nils-herrmann/clean_scirepeval huggingface dataset:
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
Authors: Chunyang Liao, Zhenxun Zhuang, Mingrui Liu

In distributed training of deep neural networks or Federated Learning (FL), people usually run Stochastic Gradient Descent (SGD) or its variants on each machine and communicate with other machines periodically. However, SGD might converge slowly in training some deep neural networks (e.g., RNN, LSTM) because of the exploding gradient issue. Gradient clipping is usually employed to address this issue in the single machine setting, but exploring this technique in the FL setting is still in its infancy: it remains mysterious whether the gradient clipping scheme can take advantage of multiple machines to enjoy parallel speedup in the FL setting. The main technical difficulty lies at dealing with nonconvex loss function, non-Lipschitz continuous gradient, and skipping communication rounds simultaneously. In this paper, we explore a relaxed-smoothness assumption of the loss landscape which LSTM was shown to satisfy in previous works, and design a communication-efficient gradient clipping algorithm. This algorithm can be run on multiple machines, where each machine employs a gradient clipping scheme and communicate with other machines after multiple steps of gradient-based updates. Our algorithm is proved to have $O\left(\frac{1}{N\epsilon^4}\right)$ iteration complexity for finding an $\epsilon$-stationary point, where $N$ is the number of machines. This indicates that our algorithm enjoys linear speedup. Our experiments on several benchmark datasets demonstrate that our algorithm indeed exhibits fast convergence speed in practice and validate our theory.
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation
Authors: Zhun Zhong, Yuyang Zhao, Gim Hee Lee, Nicu Sebe

In this paper, we consider the problem of domain generalization in semantic segmentation, which aims to learn a robust model using only labeled synthetic (source) data. The model is expected to perform well on unseen real (target) domains. Our study finds that the image style variation can largely influence the model's performance and the style features can be well represented by the channel-wise mean and standard deviation of images. Inspired by this, we propose a novel adversarial style augmentation (AdvStyle) approach, which can dynamically generate hard stylized images during training and thus can effectively prevent the model from overfitting on the source domain. Specifically, AdvStyle regards the style feature as a learnable parameter and updates it by adversarial training. The learned adversarial style feature is used to construct an adversarial image for robust model training. AdvStyle is easy to implement and can be readily applied to different models. Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains and show that we can achieve the state of the art. Moreover, AdvStyle can be employed to domain generalized image classification and produces a clear improvement on the considered datasets.
Distributed Fine-tuning of Language Models on Private Data
Authors: Vadim Popov, Mikhail Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky

One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm. In language modeling, users’ language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data. At the same time, public data can be used for obtaining general knowledge (i.e. general model of English). We study approaches to distributed fine-tuning of a general model on user private data with the additional requirements of maintaining the quality on the general data and minimization of communication costs. We propose a novel technique that significantly improves prediction quality on users’ language compared to a general model and outperforms gradient compression methods in terms of communication efficiency. The proposed procedure is fast and leads to an almost 70% perplexity reduction and 8.7 percentage point improvement in keystroke saving rate on informal English texts. Finally, we propose an experimental framework for evaluating differential privacy of distributed training of language models and show that our approach has good privacy guarantees.
Language Model Pre-training on True Negatives
Authors: Zhuosheng Zhang, hai zhao, Masao Utiyama, Eiichiro Sumita

Discriminative pre-trained language models (PrLMs) learn to predict original texts from intentionally corrupted ones. Taking the former text as positive and the latter as negative samples, the discriminative PrLM can be trained effectively for contextualized representation. However, though the training of such a type of PrLMs highly relies on the quality of the automatically constructed samples, existing PrLMs simply treat all corrupted texts as equal negative without any examination, which actually lets the resulting model inevitably suffer from the false negative issue where training is carried out on wrong data and leads to less efficiency and less robustness in the resulting PrLMs.
Thus in this work, on the basis of defining the false negative issue in discriminative PrLMs that has been ignored for a long time, we design enhanced pre-training methods to counteract false negative predictions and encourage pre-training language models on true negatives, by correcting the harmful gradient updates subject to false negative predictions. Experimental results on GLUE and SQuAD benchmarks show that our counter-false-negative pre-training methods indeed bring about better performance together with stronger robustness.
FigureQA: An Annotated Figure Dataset for Visual Reasoning
Authors: Samira Ebrahimi Kahou, Adam Atkinson, Vincent Michalski, Ákos Kádár, Adam Trischler, Yoshua Bengio

We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection. To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure. To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives. In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements. We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as strong baseline. Preliminary results indicate that the task poses a significant machine learning challenge. We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.
Safe Deep RL in 3D Environments using Human Feedback
Authors: Matthew Rahtz, Vikrant Varma, Ramana Kumar, Zachary Kenton, Shane Legg, Jan Leike

Agents should avoid unsafe behaviour during both training and deployment. This typically requires a simulator and a procedural specification of unsafe behaviour. Unfortunately, a simulator is not always available, and procedurally specifying constraints can be difficult or impossible for many real-world tasks. A recently introduced technique, ReQueST, aims to solve this problem by learning a neural simulator of the environment from safe human trajectories, then using the learned simulator to efficiently learn a reward model from human feedback. However, it is yet unknown whether this approach is feasible in complex 3D environments with feedback obtained from real humans - whether sufficient pixel-based neural simulator quality can be achieved, and whether the human data requirements are viable in terms of both quantity and quality. In this paper we answer this question in the affirmative, using ReQueST to train an agent to perform a 3D first-person object collection task using data entirely from human contractors. We show that the resulting agent exhibits an order of magnitude reduction in unsafe behaviour compared to standard reinforcement learning.
Efficiently applying attention to sequential data with the Recurrent Discounted Attention unit
Authors: Brendan Maginnis, Pierre Richemond

Recurrent Neural Networks architectures excel at processing sequences by
modelling dependencies over different timescales. The recently introduced
Recurrent Weighted Average (RWA) unit captures long term dependencies
far better than an LSTM on several challenging tasks. The RWA achieves
this by applying attention to each input and computing a weighted average
over the full history of its computations. Unfortunately, the RWA cannot
change the attention it has assigned to previous timesteps, and so struggles
with carrying out consecutive tasks or tasks with changing requirements.
We present the Recurrent Discounted Attention (RDA) unit that builds on
the RWA by additionally allowing the discounting of the past.
We empirically compare our model to RWA, LSTM and GRU units on
several challenging tasks. On tasks with a single output the RWA, RDA and
GRU units learn much quicker than the LSTM and with better performance.
On the multiple sequence copy task our RDA unit learns the task three
times as quickly as the LSTM or GRU units while the RWA fails to learn at
all. On the Wikipedia character prediction task the LSTM performs best
but it followed closely by our RDA unit. Overall our RDA unit performs
well and is sample efficient on a large variety of sequence tasks.
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Authors: Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jinfeng Yi, Mingyi Hong, Shiyu Chang, Sijia Liu

The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustifying ML models against adversarial attacks is now a major focus of research. However, nearly all existing defense methods, particularly for robust training, made the white-box assumption that the defender has the access to the details of an ML model (or its surrogate alternatives if available), e.g., its architectures and parameters. Beyond existing works, in this paper we aim to address the problem of black-box defense: How to robustify a black-box model using just input queries and output feedback? Such a problem arises in practical scenarios, where the owner of the predictive model is reluctant to share model information in order to preserve privacy. To this end, we propose a general notion of defensive operation that can be applied to black-box models, and design it through the lens of denoised smoothing (DS), a ﬁrst-order (FO) certiﬁed defense technique. To allow the design of merely using model queries, we further integrate DS with the zeroth-order (gradient-free) optimization. However, a direct implementation of zeroth-order (ZO) optimization suffers a high variance of gradient estimates, and thus leads to ineffective defense. To tackle this problem, we next propose to prepend an autoencoder (AE) to a given (black-box) model so that DS can be trained using variance-reduced ZO optimization. We term the eventual defense as ZO-AE-DS. In practice, we empirically show that ZO-AE-DS can achieve improved accuracy, certiﬁed robustness, and query complexity over existing baselines. And the effectiveness of our approach is justiﬁed under both image classiﬁcation and image reconstruction tasks.
Graph Structural Aggregation for Explainable Learning
Authors: Alexis Galland, marc lelarge

Graph neural networks have proven to be very efficient to solve several tasks in graphs such as node classification or link prediction. These algorithms that operate by propagating information from vertices to their neighbors allow one to build node embeddings that contain local information. In order to use graph neural networks for graph classification, node embeddings must be aggregated to obtain a graph representation able to discriminate among different graphs (of possibly various sizes). Moreover, in analogy to neural networks for image classification, there is a need for explainability regarding the features that are selected in the graph classification process. To this end, we introduce StructAgg, a simple yet effective aggregation process based on the identification of structural roles for nodes in graphs that we use to create an end-to-end model. Through extensive experiments we show that this architecture can compete with state-of-the-art methods. We show how this aggregation step allows us to cluster together nodes that have comparable structural roles and how these roles provide explainability to this neural network model.
A Survey on Evidential Deep Learning For Single-Pass Uncertainty Estimation
Authors: Dennis Thomas Ulmer

Popular approaches for quantifying predictive uncertainty in deep neural networks often involve a set of weights or models, for instance via ensembling or Monte Carlo Dropout. These techniques usually produce overhead by having to train multiple model instances or do not produce very diverse predictions. This survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they admit “what they don’t know” and fall back onto a prior belief. Furthermore, they allow uncertainty estimation in a single model and forward pass by parameterizing distributions over distributions. This survey recapitulates existing works, focusing on the implementation in a classification setting. Finally, we survey the application of the same paradigm to regression problems. We also provide a reflection on the strengths and weaknesses of the mentioned approaches compared to existing ones and provide the most central theoretical results in order to inform future research.
Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path
Authors: X.Y. Han, Vardan Papyan, David L. Donoho

The recently discovered Neural Collapse (NC) phenomenon occurs pervasively in today's deep net training paradigm of driving cross-entropy (CE) loss towards zero. During NC, last-layer features collapse to their class-means, both classifiers and class-means collapse to the same Simplex Equiangular Tight Frame, and classifier behavior collapses to the nearest-class-mean decision rule. Recent works demonstrated that deep nets trained with mean squared error (MSE) loss perform comparably to those trained with CE. As a preliminary, we empirically establish that NC emerges in such MSE-trained deep nets as well through experiments on three canonical networks and five benchmark datasets. We provide, in a Google Colab notebook, PyTorch code for reproducing MSE-NC and CE-NC: https://colab.research.google.com/github/neuralcollapse/neuralcollapse/blob/main/neuralcollapse.ipynb. The analytically-tractable MSE loss offers more mathematical opportunities than the hard-to-analyze CE loss, inspiring us to leverage MSE loss towards the theoretical investigation of NC. We develop three main contributions: (I) We show a new decomposition of the MSE loss into (A) terms directly interpretable through the lens of NC and which assume the last-layer classifier is exactly the least-squares classifier; and (B) a term capturing the deviation from this least-squares classifier. (II) We exhibit experiments on canonical datasets and networks demonstrating that term-(B) is negligible during training. This motivates us to introduce a new theoretical construct: the central path, where the linear classifier stays MSE-optimal for feature activations throughout the dynamics. (III) By studying renormalized gradient flow along the central path, we derive exact dynamics that predict NC.
Bayesian Relational Generative Model for Scalable Multi-modal Learning
Authors: Ehsan Hajiramezanali, Talip Ucar, Lindsay Edwards

The study of complex systems requires the integration of multiple heterogeneous and high-dimensional data types (e.g. multi-omics). However, previous generative approaches for multi-modal inputs suffer from two shortcomings. First, they are not stochastic processes, leading to poor uncertainty estimations over their predictions. This is mostly due to the computationally intensive nature of traditional stochastic processes, such as Gaussian Processes (GPs), that makes their applicability limited in multi-modal learning frameworks. Second, they are not able to effectively approximate the joint posterior distribution of multi-modal data types with various missing patterns. More precisely, their model assumptions result in miscalibrated precisions and/or computational cost of sub-sampling procedure. In this paper, we propose a class of stochastic processes that learns a graph of dependencies between samples across multi-modal data types through adopting priors over the relational structure of the given data modalities. The dependency graph in our method, multi-modal  Relational Neural Process (mRNP), not only posits distributions over the functions and naturally enables rapid adaptation to new observations by its predictive distribution, but also makes mRNP scalable to large datasets through mini-batch optimization. We also introduce mixture-of-graphs (MoG) in our model construction and show that it can address the aforementioned limitations in joint posterior approximation. Experiments on both toy regression and classification tasks using real-world datasets demonstrate the potential of mRNP for offering higher prediction accuracies as well as more robust uncertainty estimates compared to existing baselines and state-of-the-art methods.
Efficient Bi-Directional Verification of ReLU Networks via Quadratic Programming
Authors: Aleksei Kuvshinov, Stephan Guennemann

Neural networks are known to be sensitive to adversarial perturbations. To investigate this undesired behavior we consider the problem of computing the distance to the decision boundary (DtDB) from a given sample for a deep NN classifier. In this work we present an iterative procedure where in each step we solve a convex quadratic programming (QP) task. Solving the single initial QP already results in a lower bound on the DtDB and can be used as a robustness certificate of the classifier around a given sample. In contrast to currently known approaches our method also provides upper bounds used as a measure of quality for the certificate. We show that our approach provides better or competitive results in comparison with a wide range of existing techniques.
A teacher-student framework to distill future trajectories
Authors: Alexander Neitz, Giambattista Parascandolo, Bernhard Schölkopf

By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience. However, due to partial observability, stochasticity, compounding errors, and irrelevant dynamics, training to predict observations explicitly often results in poor models. Model-free techniques try to side-step the problem by learning to predict values directly. While breaking the explicit dependency on future observations can result in strong performance, this usually comes at the cost of low sample efficiency, as the abundant information about the dynamics contained in future observations goes unused. Here we take a step back from both approaches: Instead of hand-designing how trajectories should be incorporated, a teacher network learns to interpret the trajectories and to provide target activations which guide a student model that can only observe the present. The teacher is trained with meta-gradients to maximize the student's performance on a validation set. We show that our approach performs well on tasks that are difficult for model-free and model-based methods, and we study the role of every component through ablation studies.
Implicit Generative Modeling for Efficient Exploration
Authors: Neale Ratzlaff, Qinxun Bai, Li Fuxin, Wei Xu

Efficient exploration remains a challenging problem in reinforcement learning, especially for those tasks where rewards from environments are sparse. A commonly used approach for exploring such environments is to introduce some "intrinsic" reward. In this work, we focus on model uncertainty estimation as an intrinsic reward for efficient exploration. In particular, we introduce an implicit generative modeling approach to estimate a Bayesian uncertainty of the agent's belief of the environment dynamics. Each random draw from our generative model is a neural network that instantiates the dynamic function, hence multiple draws would approximate the posterior, and the variance in the future prediction based on this posterior is used as an intrinsic reward for exploration. We design a training algorithm for our generative model based on the amortized Stein Variational Gradient Descent. In experiments, we compare our implementation with state-of-the-art intrinsic reward-based exploration approaches, including two recent approaches based on an ensemble of dynamic models. In challenging exploration tasks, our implicit generative model consistently outperforms competing approaches regarding data efficiency in exploration.
Exact Stochastic Newton Method for Deep Learning: the feedforward networks case.
Authors: Fares B. Mehouachi, Chaouki Kasmi

The inclusion of second-order information into Deep Learning optimization has drawn consistent interest as a way forward to improve upon gradient descent methods. Estimating the second-order update is often convoluted and computationally expensive, which drastically limits its usage scope and forces the use of various truncations and approximations.
This work demonstrates that it is possible to solve the Newton direction in the stochastic case exactly. We consider feedforward networks as a base model, build a second-order Lagrangian which we call Sifrian, and provide a closed-form formula for the exact stochastic Newton direction under some monotonicity and regularization conditions. We propose a convexity correction to escape saddle points, and we reconsider the intrinsic stochasticity of the online learning process to improve upon the formulas.  We finally compare the performance of the developed solution with well-established training methods and show its viability as a training method for Deep Learning.
FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders
Authors: Pengyu Cheng, Weituo Hao, Siyang Yuan, Shijing Si, Lawrence Carin

Pretrained text encoders, such as BERT, have been applied increasingly in various natural language processing (NLP) tasks, and have recently demonstrated significant performance gains. However, recent studies have demonstrated the existence of social bias in these pretrained NLP models. Although prior works have made progress on word-level debiasing, improved sentence-level fairness of pretrained encoders still lacks exploration. In this paper, we proposed the first neural debiasing method for a pretrained sentence encoder, which transforms the pretrained encoder outputs into debiased representations via a fair filter (FairFil) network. To learn the FairFil, we introduce a contrastive learning framework that not only minimizes the correlation between filtered embeddings and bias words but also preserves rich semantic information of the original sentences. On real-world datasets, our FairFil effectively reduces the bias degree of pretrained text encoders, while continuously showing desirable performance on downstream tasks. Moreover, our post hoc method does not require any retraining of the text encoders, further enlarging FairFil's application space.