nils-herrmann/clean_scirepeval huggingface dataset:
Discourse-Based Evaluation of Language Understanding
Authors: Damien Sileo, Tim Van-De-Cruys, Camille Pradel, Philippe Muller

New models for natural language understanding have made unusual progress recently, leading to claims of universal text representations. However, current benchmarks are predominantly targeting semantic phenomena;  we make the case that discourse and pragmatics need to take center stage in the evaluation of natural language understanding.
We introduce DiscEval, a new benchmark for the evaluation of natural language understanding, that unites 11 discourse-focused evaluation datasets. 
DiscEval can be used as supplementary training data in a multi-task learning setup, and is publicly available, alongside the code for gathering and preprocessing the datasets.
Using our evaluation suite, we show that natural language inference, a widely used pretraining task, does not result in genuinely universal representations, which opens a new challenge for multi-task learning.
PCNN: Environment Adaptive Model Without Finetuning
Authors: Boyuan Feng, Kun Wan, Shu Yang, Yufei Ding

Convolutional Neural Networks (CNNs) have achieved tremendous success for many computer vision tasks, which shows a promising perspective of deploying CNNs on mobile platforms. An obstacle to this promising perspective is the tension between intensive resource consumption of CNNs and limited resource budget on mobile platforms. Existing works generally utilize a simpler architecture with lower accuracy for a higher energy-efficiency, \textit{i.e.}, trading accuracy for resource consumption. An emerging opportunity to both increasing accuracy and decreasing resource consumption is \textbf{class skew}, \textit{i.e.}, the strong temporal and spatial locality of the appearance of classes. However, it is challenging to efficiently utilize the class skew due to both the frequent switches and the huge number of class skews. Existing works use transfer learning to adapt the model towards the class skew during runtime, which consumes resource intensively. In this paper, we propose \textbf{probability layer}, an \textit{easily-implemented and highly flexible add-on module} to adapt the model efficiently during runtime \textit{without any fine-tuning} and achieving an \textit{equivalent or better} performance than transfer learning. Further, both \textit{increasing accuracy} and \textit{decreasing resource consumption} can be achieved during runtime through the combination of probability layer and pruning methods.
Fieldwise Factorized Networks for Tabular Data Classification
Authors: Chen Almagor, Yedid Hoshen

Tabular data is one of the most common data-types in machine learning, however, deep neural networks have not yet convincingly outperformed classical baselines on such datasets. In this paper, we first investigate the theoretical connection between neural network and factorization machine techniques, and present fieldwise factorized neural networks (F2NN), a neural network architecture framework that is aimed for tabular classification. Our framework learns high-dimensional field representations by a low-rank factorization, and handles both categorical and numerical fields. Furthermore, we show that simply by changing our penultimate activation function, the framework recovers a range of popular tabular classification methods. We evaluate our method against state-of-the-art tabular baselines, including tree-based and deep neural network methods, on a range of tasks. Our findings suggest that our theoretically grounded but simple and shallow neural network architecture achieves as strong or better results than more complex methods.
Graph-Relational Domain Adaptation
Authors: Zihao Xu, Hao He, Guang-He Lee, Bernie Wang, Hao Wang

Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; therefore it may be beneficial for nearby domains, but not necessarily for distant domains. In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e.g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure. We generalize the existing adversarial learning framework with a novel graph discriminator using encoding-conditioned graph embeddings. Theoretical analysis shows that at equilibrium, our method recovers classic domain adaptation when the graph is a clique, and achieves non-trivial alignment for other types of graphs. Empirical results show that our approach successfully generalizes uniform alignment, naturally incorporates domain information represented by graphs, and improves upon existing domain adaptation methods on both synthetic and real-world datasets.
BLOOD: Bi-level Learning Framework for Out-of-distribution Generalization
Authors: Jun-Hyun Bae, Inchul Choi, Minho Lee

Empirical risk minimization (ERM) based machine learning algorithms have suffered from weak generalization performance on the out-of-distribution (OOD) data when the training data are collected from separate environments with unknown spurious correlations. To address this problem, previous works either exploit prior human knowledge for biases in the dataset or apply the two-stage process, which re-weights spuriously correlated samples after they were identified by the biased classifier. However, most of them fail to remove multiple types of spurious correlations that exist in training data. In this paper, we propose a novel bi-level learning framework for OOD generalization, which can effectively remove multiple unknown types of biases without any prior bias information or separate re-training steps of a model. In our bi-level learning framework, we uncover spurious correlations in the inner-loop with shallow model-based predictions and dynamically re-group the data to leverage the group distributionally robust optimization method in the outer-loop, minimizing the worst-case risk across all batches. Our main idea applies the unknown bias discovering process to the group construction method of the group DRO algorithm in a bi-level optimization setting and provides a unified de-biasing framework that can handle multiple types of biases in data. In empirical evaluations on both synthetic and real-world datasets, our framework shows superior OOD performance compared to all other state-of-the-art OOD methods by a large margin. Furthermore, it successfully removes multiple types of biases in the training data groups that most other OOD models fail.
Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization
Authors: Minhao Cheng, Zhe Gan, Yu Cheng, Shuohang Wang, Cho-Jui Hsieh, Jingjing Liu

Adversarial training is a commonly used technique to improve model robustness against adversarial examples. Despite its success as a defense mechanism, adversarial training often fails to generalize well to unperturbed test data. While previous work assumes it is caused by the discrepancy between robust and non-robust features, in this paper, we introduce \emph{Adversarial Masking}, a new hypothesis that this trade-off is caused by different feature maskings applied. Specifically, the rescaling operation in the batch normalization layer, when combined together with ReLU activation, serves as a feature masking layer to select different features for model training. By carefully manipulating different maskings, a well-balanced trade-off can be achieved between model performance on unperturbed and perturbed data. Built upon this hypothesis, we further propose Robust Masking (RobMask),  which constructs unique masking for every specific attack perturbation by learning a set of primary adversarial feature maskings. By incorporating different feature maps after the masking, we can distill better features to help model generalization. Sufficiently, adversarial training can be treated as an effective regularizer to achieve better generalization. Experiments on multiple benchmarks demonstrate that RobMask achieves significant improvement on clean test accuracy compared to strong state-of-the-art baselines.
WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia
Authors: Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong, Francisco Guzmán

We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 85 languages, including several dialects or low-resource languages.  We do not limit the extraction process to alignments with English, but systematically consider all possible language pairs.  In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English.  This corpus of parallel sentences is freely available (URL anonymized)
  
To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs.  The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English.
DC3: A learning method for optimization with hard constraints
Authors: Priya L. Donti, David Rolnick, J Zico Kolter

Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infeasible solutions. In this work, we present Deep Constraint Completion and Correction (DC3), an algorithm to address this challenge. Specifically, this method enforces feasibility via a differentiable procedure, which implicitly completes partial solutions to satisfy equality constraints and unrolls gradient-based corrections to satisfy inequality constraints. We demonstrate the effectiveness of DC3 in both synthetic optimization tasks and the real-world setting of AC optimal power flow, where hard constraints encode the physics of the electrical grid. In both cases, DC3 achieves near-optimal objective values while preserving feasibility.
Hierarchical Attention: What Really Counts in Various NLP Tasks
Authors: Zehao Dou, Zhihua Zhang

Attention mechanisms in sequence to sequence models have shown great ability and wonderful performance in various natural language processing  (NLP)  tasks, such as sentence embedding, text generation, machine translation, machine reading comprehension, etc. Unfortunately, existing attention mechanisms only learn either high-level or low-level features. In this paper, we think that the lack of hierarchical mechanisms is a bottleneck in improving the performance of the attention mechanisms, and propose a novel Hierarchical Attention Mechanism (Ham) based on the weighted sum of different layers of a multi-level attention. 
Ham achieves a state-of-the-art BLEU score of 0.26 on Chinese poem generation task and a nearly 6.5% averaged improvement compared with the existing machine reading comprehension models such as BIDAF and Match-LSTM. Furthermore, our experiments and theorems reveal that Ham has greater generalization and representation ability than existing attention mechanisms.
Subspace State-Space Identification and Model Predictive Control of Nonlinear Dynamical Systems Using Deep Neural Network with Bottleneck
Authors: Ichiro Maruta, Keito Yamada, Kenji Fujimoto

A novel nonlinear system identification method that produces state estimator and predictor directly usable for model predictive control (MPC) is proposed in this paper. The main feature of the proposed method is that it uses a neural network with a bottleneck layer between the state estimator and predictor to represent the input-output dynamics, and it is proven that the state of the dynamical system can be extracted from the bottleneck layer based on the observability of the target system. The training of the network is shown to be a natural nonlinear extension of the subspace state-space system identification method established for linear dynamical systems. This correspondence gives interpretability to the resulting model based on linear control theory. The usefulness of the proposed method and the interpretability of the model are demonstrated through an illustrative example of MPC.
AN ATTENTION-BASED DEEP NET FOR LEARNING TO RANK
Authors: Diego Klabjan, Baiyang Wang

In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query. Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism. This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion. The embeddings are trained with convolutional neural networks or the word2vec model. We demonstrate the performance of this model with image retrieval and text querying data sets.
Mode Connectivity and Sparse Neural Networks
Authors: Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin

We uncover a connection between two seemingly unrelated empirical phenomena: mode connectivity and sparsity. On the one hand, there is growing catalog of situations where, across multiple runs, SGD learns weights that fall into minima that are connected (mode connectivity). A striking example is described by Nagarajan & Kolter (2019). They observe that test error on MNIST does not change along the linear path connecting the end points of two independent SGD runs, starting from the same random initialization. On the other hand, there is the lottery ticket hypothesis of Frankle & Carbin (2019), where dense, randomly initialized networks have sparse subnetworks capable of training in isolation to full accuracy.

However, neither phenomenon scales beyond small vision networks. We start by proposing a technique to find sparse subnetworks after initialization. We observe that these subnetworks match the accuracy of the full network only when two SGD runs for the same subnetwork are connected by linear paths with the no change in test error. Our findings connect the existence of sparse subnetworks that train to high accuracy with the dynamics of optimization via mode connectivity. In doing so, we identify analogues of the phenomena uncovered by Nagarajan & Kolter and Frankle & Carbin in ImageNet-scale architectures at state-of-the-art sparsity levels.
Modeling Adversarial Noise for Adversarial Defense
Authors: Dawei Zhou, Nannan Wang, Bo Han, Tongliang Liu

Deep neural networks have been demonstrated to be vulnerable to adversarial noise, promoting the development of defense against adversarial attacks. Motivated by the fact that adversarial noise contains well-generalizing features and that the relationship between adversarial data and natural data can help infer natural data and make reliable predictions, in this paper, we study to model adversarial noise by learning the transition relationship between adversarial labels (i.e. the flipped labels used to generate adversarial data) and natural labels (i.e. the ground truth labels of the natural data). Specifically, we introduce an instance-dependent transition matrix to relate adversarial labels and natural labels, which can be seamlessly embedded with the target model (enabling us to model stronger adaptive adversarial noise). Empirical evaluations demonstrate that our method could effectively improve adversarial accuracy.
Multi-Task Learning for Document Ranking and Query Suggestion
Authors: Wasi Uddin Ahmad, Kai-Wei Chang, Hongning Wang

We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search. It consists of two major components, a document ranker, and a query recommender. Document ranker combines current query and session information and compares the combined representation with document representation to rank the documents. Query recommender tracks users' query reformulation sequence considering all previous in-session queries using a sequence to sequence approach. As both tasks are driven by the users' underlying search intent, we perform joint learning of these two components through session recurrence, which encodes search context and intent. Extensive comparisons against state-of-the-art document ranking and query suggestion algorithms are performed on the public AOL search log, and the promising results endorse the effectiveness of the joint learning framework.
Deep Character-Level Neural Machine Translation By Learning Morphology
Authors: Shenjian Zhao, Zhihua Zhang

Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems. However, the use of large vocabulary becomes the bottleneck in both training and improving the performance. In this paper, we propose a novel architecture which learns morphology by using two recurrent networks and a hierarchical decoder which translates at character level. This gives rise to a deep character-level model consisting of six recurrent networks. Such a deep model has two major advantages. It avoids the large vocabulary issue radically; at the same time, it is more efficient in training than word-based models. Our model obtains a higher BLEU score than the bpe-based model after training for one epoch on En-Fr and En-Cs translation tasks. Further analyses show that our model is able to learn morphology.
Filter Training and Maximum Response: Classification via Discerning
Authors: Lei Gu

This report introduces a training and recognition scheme, in which classification is realized via class-wise discerning. Trained with datasets whose labels are randomly shuffled except for one class of interest, a neural network learns class-wise parameter values, and remolds itself from a feature sorter into feature filters, each of which discerns objects belonging to one of the classes only. Classification of an input can be inferred from the maximum response of the filters. A multiple check with multiple versions of filters can diminish fluctuation and yields better performance. This scheme of discerning, maximum response and multiple check is a method of general viability to improve performance of feedforward networks, and the filter training itself is a promising feature abstraction procedure. In contrast to the direct sorting, the scheme mimics the classification process mediated by a series of one component picking.
Out-of-distribution Detection in Few-shot Classification
Authors: Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou, Chia-Cheng Liu, Richard Zemel

In many real-world settings, a learning model must perform few-shot classification: learn to classify examples from unseen classes using only a few labeled examples per class.
Additionally, to be safely deployed, it should have the ability to detect out-of-distribution inputs: examples that do not belong to any of the classes.
While both few-shot classification and out-of-distribution detection are popular topics,
their combination has not been studied. In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.  Then, we propose two new methods for this task and investigate their performance.
In sum, we establish baseline out-of-distribution detection results using standard metrics on new benchmark datasets and show improved results with our proposed methods.
Adversarial Feature Desensitization
Authors: Pouya Bashivan, Mojtaba Faramarzi, Touraj Laleh, Blake Aaron Richards, Irina Rish

Deep neural networks can now perform many tasks that were once thought to be only feasible for humans. While reaching impressive performance under standard settings, such networks are known to be  susceptible to  adversarial attacks -- slight but carefully constructed perturbations of the inputs which drastically decrease the network performance. Here we propose a new way to improve the network robustness against adversarial attacks by   focusing on robust representation learning based on  adversarial training procedure, called here Adversarial Feature Desensitization (AFD). AFD desensitizes the representation via an adversarial game between the embedding network and an adversarial discriminator introduced on top of  the standard predictive model, which is trained to distinguish between the clean and perturbed inputs from their high-level representations. Our method substantially improves the state-of-the-art in robust classification on MNIST, CIFAR10, and CIFAR100 datasets. More importantly, we demonstrate that AFD has better generalization ability than previous methods, as the learned features maintain their robustness across a wide range of perturbations, including perturbations not seen during training. These results indicate that reducing feature sensitivity is a promising approach for ameliorating the problem of adversarial attacks in deep neural networks.
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
Authors: Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson

Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games. Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.