nils-herrmann/clean_scirepeval huggingface dataset:
FEED: Feature-level Ensemble Effect for knowledge Distillation
Authors: SeongUk Park, Nojun Kwak

This paper proposes a versatile and powerful training algorithm named Feature-level Ensemble Effect for knowledge Distillation(FEED), which is inspired by the work of factor transfer. The factor transfer is one of the knowledge transfer methods that improves the performance of a student network with a strong teacher network. It transfers the knowledge of a teacher in the feature map level using high-capacity teacher network, and our training algorithm FEED is an extension of it. FEED aims to transfer ensemble knowledge, using either multiple teachers in parallel or multiple training sequences. Adapting the peer-teaching framework, we introduce a couple of training algorithms that transfer ensemble knowledge to the student at the feature map level, both of which help the student network find more generalized solutions in the parameter space. Experimental results on CIFAR-100 and ImageNet show that our method, FEED, has clear performance enhancements,without introducing any additional parameters or computations at test time.
A framework of deep neural networks via the solution operator of partial differential equations
Authors: Wenqi Tao, Zuoqiang Shi

There is a close connection between deep neural networks (DNN) and partial differential equations (PDEs). Many DNN architectures can be modeled by PDEs and have been proposed in the literature. However, their neural network design space is restricted due to the specific form of PDEs, which prevents the design of more effective neural network structures. In this paper, we attempt to derive a general form of PDEs for the design of ResNet-like DNN. To achieve this goal, we first formulate DNN as an adjustment operator applied on the base classifier. Then based on several reasonable assumption, we show the adjustment operator for ResNet-like DNN is the solution operator of PDEs. To show the effectiveness for general form of PDEs, we show that several effective networks can be interpreted by our general form of PDEs and design a training method motivated by PDEs theory to train DNN models for better robustness and less chance of overfitting. Theoretically, we prove that the robustness of DNN trained with our method is certifiable and our training method reduces the generalization gap for DNN. Furthermore, we demonstrate that DNN trained with our method can achieve better generalization and is more resistant to adversarial perturbations than baseline model.
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
Authors: Daniel J Mankowitz, Dan Andrei Calian, Rae Jeong, Cosmin Paduraru, Nicolas Heess, Sumanth Dathathri, Martin Riedmiller, Timothy Mann

Many real-world physical control systems are required to satisfy constraints upon deployment. Furthermore, real-world systems are often subject to effects such as non-stationarity, wear-and-tear, uncalibrated sensors and so on. Such effects effectively perturb the system dynamics and can cause a policy trained successfully in one domain to perform poorly when deployed to a perturbed version of the same domain. This can affect a policy's ability to maximize future rewards as well as the extent to which it satisfies constraints. We refer to this as constrained model misspecification. We present an algorithm with theoretical guarantees that mitigates this form of misspecification, and showcase its performance in multiple Mujoco tasks from the Real World Reinforcement Learning (RWRL) suite.
N-Ary Quantization for CNN Model Compression and Inference Acceleration
Authors: Günther Schindler, Wolfgang Roth, Franz Pernkopf, Holger Fröning

The tremendous memory and computational complexity of Convolutional Neural Networks (CNNs) prevents the inference deployment on resource-constrained systems. As a result, recent research focused on CNN optimization techniques, in particular quantization, which allows weights and activations of layers to be represented with just a few bits while achieving impressive prediction performance. However, aggressive quantization techniques still fail to achieve full-precision prediction performance on state-of-the-art CNN architectures on large-scale classification tasks. In this work we propose a method for weight and activation quantization that is scalable in terms of quantization levels (n-ary representations) and easy to compute while maintaining the performance close to full-precision CNNs. Our weight quantization scheme is based on trainable scaling factors and a nested-means clustering strategy which is robust to weight updates and therefore exhibits good convergence properties. The flexibility of nested-means clustering enables exploration of various n-ary weight representations with the potential of high parameter compression. For activations, we propose a linear quantization strategy that takes the statistical properties of batch normalization into account. We demonstrate the effectiveness of our approach using state-of-the-art models on ImageNet.
Out-of-Distribution Image Detection Using the Normalized Compression Distance
Authors: Sehun Yu, Donga Lee, Hwanjo Yu

On detection of the out-of-distribution images, whose underlying distribution is different from that of the training dataset, we tackle to apply out-of-distribution detection methods to already deployed convolutional neural networks. Most recent approaches have to utilize out-of-distribution samples for validation or retrain the model, which makes it less practical for real-world applications. We propose a novel out-of-distribution detection method MALCOM, which neither uses any out-of-distribution samples nor retrain the model. Inspired by the method using the global average pooling on the feature maps of the convolutional neural networks, the goal of our method is to extract informative sequential patterns from the feature maps. To this end, we introduce a similarity metric which focuses on the shared patterns between two sequences. In short, MALCOM uses both the global average and spatial pattern of the feature maps to accurately identify out-of-distribution samples.
Hierarchical Bayesian Modeling for Clustering Sparse Sequences in the Context of Group Profiling
Authors: Ishani Chakraborty

This paper proposes a hierarchical Bayesian model for clustering sparse sequences.This is a mixture model and does not need the data to be represented by a Gaussian mixture and that gives significant modelling freedom.It also generates a very interpretable profile for the discovered latent groups.The data that was used for the work have been contributed by a restaurant loyalty program company. The data is a collection of sparse sequences where each entry of each sequence is the number of user visits of one week to some restaurant. This algorithm successfully clustered the data and calculated the expected user affiliation in each cluster.
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation
Authors: Fuli Luo, Wei Wang, Jiahao Liu, Yijia Liu, Bin Bi, Songfang Huang, Fei Huang, Luo Si

Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks. They train either an encoder-only Transformer mainly for understanding tasks, or an encoder-decoder Transformer specifically for generation tasks, ignoring the correlation between the two tasks and frameworks. In contrast, this paper presents a variable encoder-decoder (VECO) pre-training approach to unify the two mainstreams in both model architectures and pre-training tasks. VECO splits the standard Transformer block into several sub-modules trained with both inner-sequence and cross-sequence masked language modeling, and correspondingly reorganizes certain sub-modules for understanding and generation tasks during inference. Such a workflow not only ensures to train the most streamlined parameters necessary for two kinds of tasks, but also enables them to boost each other via sharing common sub-modules. As a result, VECO delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark covering text classification, sequence labeling, question answering, and sentence retrieval. For generation tasks, VECO also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1~2 BLEU.
Efficient Competitive Self-Play Policy Optimization
Authors: Yuanyi Zhong, Yuan Zhou, Jian Peng

Reinforcement learning from self-play has recently reported many successes. Self-play, where the agents compete with themselves, is often used to generate training data for iterative policy improvement. In previous work, heuristic rules are designed to choose an opponent for the current learner. Typical rules include choosing the latest agent, the best agent, or a random historical agent. However, these rules may be inefficient in practice and sometimes do not guarantee convergence even in the simplest matrix games. This paper proposes a new algorithmic framework for competitive self-play reinforcement learning in two-player zero-sum games. We recognize the fact that the Nash equilibrium coincides with the saddle point of the stochastic payoff function, which motivates us to borrow ideas from classical saddle point optimization literature. Our method simultaneously trains several agents and intelligently takes each other as opponents based on a simple adversarial rule derived from a principled perturbation-based saddle optimization method. We prove theoretically that our algorithm converges to an approximate equilibrium with high probability in convex-concave games under standard assumptions. Beyond the theory, we further show the empirical superiority of our method over baseline methods relying on the aforementioned opponent-selection heuristics in matrix games, grid-world soccer, Gomoku, and simulated robot sumo, with neural net policy function approximators.
On the Landscape of Sparse Linear Networks
Authors: Dachao Lin, Ruoyu Sun, Zhihua Zhang

Network pruning, or sparse network has a long history and practical significance in modern applications. Although the loss functions of neural networks may yield bad landscape due to non-convexity, we focus on linear activation which already owes benign landscape. With no unrealistic assumption, we conclude the following statements for the squared loss objective of general sparse linear neural networks: 1) every local minimum is a global minimum for scalar output with any sparse structure, or non-intersected sparse first layer and dense other layers with orthogonal training data; 2) sparse linear networks have sub-optimal local-min for only sparse first layer due to low rank constraint, or output larger than three dimensions due to the global minimum of a sub-network. Overall, sparsity breaks the normal structure, cutting out the decreasing path in original fully-connected networks.
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning
Authors: Rasheed El-Bouri, Tingting Zhu, David A. Clifton

Given the abundance and ease of access of personal data today, individual privacy has become of paramount importance, particularly in the healthcare domain. In this work, we aim to utilise patient data extracted from multiple hospital data centres to train a machine learning model without sacrificing patient privacy. We develop a scheduling algorithm in conjunction with a student-teacher algorithm that is deployed in a federated manner. This allows a central model to learn from batches of data at each federal node. The teacher acts between data centres to update the main task (student) algorithm using the data that is stored in the various data centres. We show that the scheduler, trained using meta-gradients, can effectively organise training and as a result train a machine learning model on a diverse dataset without needing explicit access to the patient data. We achieve state-of-the-art performance and show how our method overcomes some of the problems faced in the federated learning such as node poisoning. We further show how the scheduler can be used as a mechanism for transfer learning, allowing different teachers to work together in training a student for state-of-the-art performance.
Generalized Graph Embedding Models
Authors: Qiao Liu, Xiaohui Yang, Rui Wan, Shouzhong Tu, Zufeng Wu

Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of research interest recently, but only ad hoc solutions have been obtained this far. In this paper, we conjecture that the one-shot supervised learning mechanism is a bottleneck in improving the performance of the graph embedding learning algorithms, and propose to extend this by introducing a multi-shot unsupervised learning framework. Empirical results on several real-world data set show that the proposed model consistently and significantly outperforms existing state-of-the-art approaches on knowledge base completion and graph based multi-label classification tasks.
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks
Authors: Aye Phyu Phyu Aung, Xinrun Wang, Runsheng Yu, Bo An, Senthilnath Jayavelu, Xiaoli Li

In this paper, we propose a new approach to train Generative Adversarial Networks (GAN) where we deploy a double-oracle framework using the generator and discriminator oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. Training GANs is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as GANs have a large-scale strategy space. In DO-GAN, we extend the double oracle framework to GANs. We first generalize the player strategies as the trained models of generator and discriminator from the best response oracles. We then compute the meta-strategies using a linear program. Next, we prune the weakly-dominated player strategies to keep the oracles from becoming intractable. We apply our framework to established architectures such as vanilla GAN, Deep Convolutional GAN, Spectral Normalization GAN and Stacked GAN. Finally, we conduct evaluations on MNIST, CIFAR-10 and CelebA datasets and show that DO-GAN variants have significant improvements in both subjective qualitative evaluation and quantitative metrics, compared with their respective GAN architectures.
DCoM: A Deep Column Mapper for Semantic Data Type Detection
Authors: Subhadip Maji, Swapna sourav Rout, Sudeep Choudhary

Detection of semantic data types is a very crucial task in data science for automated data cleaning, schema matching, data discovery, semantic data type normalization and sensitive data identification. Existing methods include regular expression-based or dictionary lookup-based methods that are not robust to dirty as well unseen data and are limited to a very less number of semantic data types to predict. Existing Machine Learning methods extract a large number of engineered features from data and build logistic regression, random forest or feedforward neural network for this purpose. In this paper, we introduce DCoM, a collection of multi-input NLP-based deep neural networks to detect semantic data types where instead of extracting a large number of features from the data, we feed the raw values of columns (or instances) to the model as texts. We train DCoM on 686,765 data columns extracted from the VizNet corpus with 78 different semantic data types. DCoM outperforms other contemporary results with a quite significant margin on the same dataset achieving a support-weighted F1 score of 0.925.
Graph View-Consistent Learning Network
Authors: Zhuolin Liao, Kun Zhan

Recent years, methods based on neural networks have made great achievements in solving large and complex graph problems. However, high efficiency of these methods depends on large training and validation sets, while the acquisition of ground-truth labels is expensive and time-consuming. In this paper, a graph view-consistent learning network (GVCLN) is specially designed for the semi-supervised learning when the number of the labeled samples is very small. We fully exploit the neighborhood aggregation capability of GVCLN and use dual views to obtain different representations. Although the two views have different viewing angles, their observation objects are the same, so their observation representations need to be consistent. For view-consistent representations between two views, two loss functions are designed besides a supervised loss. The supervised loss uses the known labeled set, while a view-consistent loss is applied to the two views to obtain the consistent representation and a pseudo-label loss is designed by using the common high-confidence predictions. GVCLN with these loss functions can obtain the view-consistent representations of the original feature. We also find that preprocessing the node features with specific filter before training is good for subsequent classification tasks. Related experiments have been done on the three citation network datasets of Cora, Citeseer, and PubMed. On several node classification tasks, GVCLN achieves state-of-the-art performance.
Optimization Variance:  Exploring Generalization Properties of DNNs
Authors: Xiao Zhang, Dongrui Wu, Haoyi Xiong, Bo Dai

Unlike the conventional wisdom in statistical learning theory, the test error of a deep neural network (DNN) often demonstrates double descent: as the model complexity increases, it first follows a classical U-shaped curve and then shows a second descent. Through bias-variance decomposition, recent studies revealed that the bell-shaped variance is the major cause of model-wise double descent (when the DNN is widened gradually). This paper investigates epoch-wise double descent, i.e., the test error of a DNN also shows double descent as the number of training epochs increases. Specifically, we extend the bias-variance analysis to epoch-wise double descent, and reveal that the variance also contributes the most to the zero-one loss, as in model-wise double descent. Inspired by this result, we propose a novel metric called optimization variance to measure the diversity of model updates caused by the stochastic gradients of random training batches drawn in the same iteration. This metric can be estimated using samples from the training set only but correlates well with the test error. The proposed optimization variance can be used to predict the generalization ability of a DNN, and hence early stopping can be achieved without using any validation set.
RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning
Authors: Zhuo Wang, Wei Zhang, Ning Liu, Jianyong Wang

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to tackle these issues, but they sacrifice the model interpretability. In this paper, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. An improved design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on 9 small and 4 large data sets show that RRL outperforms the competitive approaches, has low complexity close to the simple decision trees, and is rational for its main technical contributions.
Transductive Universal Transport for Zero-Shot Action Recognition
Authors: Pascal Mettes

This work addresses the problem of recognizing action categories in videos for which no training examples are available. The current state-of-the-art enables such a zero-shot recognition by learning universal mappings from videos to a shared semantic space, either trained on large-scale seen actions or on objects. While effective, universal action and object models are biased to their seen categories. Such biases are further amplified due to biases between seen and unseen categories in the semantic space. The amplified biases result in many unseen action categories simply never being selected during inference, hampering zero-shot progress. We seeks to address this limitation and introduce transductive universal transport for zero-shot action recognition. Our proposal is to re-position unseen action embeddings through transduction, \ie by using the distribution of the unlabelled test set. For universal action models, we first find an optimal mapping from unseen actions to the mapped test videos in the shared hyperspherical space. We then define target embeddings as weighted Fr\'echet means, with the weights given by the transport couplings. Finally, we re-position unseen action embeddings along the geodesic between the original and target, as a form of semantic regularization. For universal object models, we outline a weighted transport variant from unseen action embeddings to object embeddings directly. Empirically, we show that our approach directly boosts universal action and object models, resulting in state-of-the-art performance for zero-shot classification and spatio-temporal localization.
Cluster-Former: Clustering-based Sparse Transformer for Question Answering
Authors: Shuohang Wang, Luowei Zhou, Zhe Gan, Yen-Chun Chen, Yuwei Fang, Siqi Sun, Yu Cheng, Jingjing Liu

Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected contextual encoding over input tokens. 
However, despite its effectiveness in modeling short sequences, self-attention suffers when handling inputs with extreme long-range dependencies, as its complexity grows quadratically with respect to the sequence length.
Therefore, long sequences are often encoded by Transformer in chunks using a sliding window.
In this paper, we propose Cluster-Former, a novel clustering-based sparse Transformer to perform attention across chunked sequences. The proposed framework is pivoted on two unique types of Transformer layer: Sliding-Window Layer and Cluster-Former Layer, which encode local sequence information and global context jointly and iteratively.
This new design allows information integration beyond local windows, which is especially beneficial for question answering (QA) tasks that rely on long-range dependencies. Experiments show that Cluster-Former achieves state-of-the-art performance on several major QA benchmarks.