nils-herrmann/clean_scirepeval huggingface dataset:
Orthogonalising gradients to speedup neural network optimisation
Authors: Mark Tuddenham, Adam Prugel-Bennett, Jonathon Hare

The optimisation of neural networks can be sped up by orthogonalising the gradients before the optimisation step, ensuring the diversification of the learned representations. We hypothesize that components in the same layer learn the same representations at the beginning of learning. To prevent this we orthogonalise the gradients of the components with respect to each other.
Our method of orthogonalisation allows the weights to be used more flexibly, in contrast to restricting the weights to an orthogonalised sub-space. We tested this method on ImageNet and CIFAR-10 resulting in a large decrease in learning time, and also obtain a speed-up on the semi-supervised learning BarlowTwins. We obtain similar accuracy to SGD without fine-tuning and better accuracy for naïvely chosen hyper-parameters.
Universal Attacks on Equivariant Networks
Authors: Amit Deshpande, Sandesh Kamath, K V Subrahmanyam

Adversarial attacks on neural networks perturb the input at test time in order to fool trained and deployed neural network models. Most attacks such as gradient-based Fast Gradient Sign Method (FGSM) by Goodfellow et al. 2015 and DeepFool by Moosavi-Dezfooli et al. 2016 are input-dependent, small, pixel-wise perturbations, and they give different attack directions for different inputs. On the other hand, universal adversarial attacks are input-agnostic and the same attack works for most inputs. Translation or rotation-equivariant neural network models provide one approach to prevent universal attacks based on simple geometric transformations. In this paper, we observe an interesting spectral property shared by all of the above input-dependent, pixel-wise adversarial attacks on translation and rotation-equivariant networks. We exploit this property to get a single universal attack direction that fools the model on most inputs. Moreover, we show how to compute this universal attack direction using principal components of the existing input-dependent attacks on a very small sample of test inputs. We complement our empirical results by a theoretical justification, using matrix concentration inequalities and spectral perturbation bounds. We also empirically observe that the top few principal adversarial attack directions are nearly orthogonal to the top few principal invariant directions.
Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search
Authors: Anji Liu, Jianshu Chen, Mingze Yu, Yu Zhai, Xuewen Zhou, Ji Liu

Monte Carlo Tree Search (MCTS) algorithms have achieved great success on many challenging benchmarks (e.g., Computer Go). However, they generally require a large number of rollouts, making their applications costly. Furthermore, it is also extremely challenging to parallelize MCTS due to its inherent sequential nature: each rollout heavily relies on the statistics (e.g., node visitation counts) estimated from previous simulations to achieve an effective exploration-exploitation tradeoff. In spite of these difficulties, we develop an algorithm, WU-UCT, to effectively parallelize MCTS, which achieves linear speedup and exhibits only limited performance loss with an increasing number of workers. The key idea in WU-UCT is a set of statistics that we introduce to track the number of on-going yet incomplete simulation queries (named as unobserved samples). These statistics are used to modify the UCT tree policy in the selection steps in a principled manner to retain effective exploration-exploitation tradeoff when we parallelize the most time-consuming expansion and simulation steps. Experiments on a proprietary benchmark and the Atari Game benchmark demonstrate the linear speedup and the superior performance of WU-UCT comparing to existing techniques.
Multi-hop Attention Graph Neural Network
Authors: Guangtao Wang, Zhitao Ying, Jing Huang, Jure Leskovec

Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning task. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes. However, such attention mechanism does not account for nodes that are not directly connected but provide important network context, which could lead to improved predictive performance. Here we propose Multi-hop Attention Graph Neural Network (MAGNA), a principled way to incorporate multi-hop context information into attention computation, enabling long-range interactions at every layer of the GNN.  To compute attention between nodes that are not directly connected,
MAGNA diffuses the attention scores across the network, which increases the ''receptive field'' for every layer of the GNN.
Unlike previous approaches, MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. This helps MAGNA capture large-scale structural information in every layer, and learn more informative attention. Experimental results on node classification as well as the knowledge graph completion benchmarks show that MAGNA achieves state-of-the-art results: MAGNA achieves up to 5.7% relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. MAGNA also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion  MAGNA advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics.
Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks
Authors: Xiaojun Ma, Ziyao Li, Lingjun Xu, Guojie Song, Yi Li, Chuan Shi

Different nodes in a graph neighborhood generally yield different importance. In previous work of Graph Convolutional Networks (GCNs), such differences are typically modeled with attention mechanisms. However, as we prove in our paper, soft attention weights suffer from over-smoothness in large neighborhoods. To address this weakness, we introduce a novel framework of conducting graph convolutions, where nodes are discretely selected among multi-hop neighborhoods to construct adaptive receptive fields (ARFs). ARFs enable GCNs to get rid of the over-smoothness of soft attention weights, as well as to efficiently explore long-distance dependencies in graphs. We further propose GRARF (GCN with Reinforced Adaptive Receptive Fields) as an instance, where an optimal policy of constructing ARFs is learned with reinforcement learning. GRARF achieves or matches state-of-the-art performances on public datasets from different domains. Our further analysis corroborates that GRARF is more robust than attention models against neighborhood noises.
Unsupervised Document Representation using Partition Word-Vectors Averaging
Authors: Vivek Gupta, Ankit Kumar Saw, Partha Pratim Talukdar, Praneeth Netrapalli

Learning effective document-level representation is essential in many important NLP tasks such as document classification, summarization, etc. Recent research has shown that simple weighted averaging of word vectors is an effective way to represent sentences, often outperforming complicated seq2seq neural models in many tasks. While it is desirable to use the same method to represent documents as well, unfortunately, the effectiveness is lost when representing long documents involving multiple sentences. One reason for this degradation is due to the fact that a longer document is likely to contain words from many different themes (or topics), and hence creating a single vector while ignoring all the thematic structure is unlikely to yield an effective representation of the document. This problem is less acute in single sentences and other short text fragments where presence of a single theme/topic is most likely. To overcome this problem, in this paper we present PSIF, a partitioned word averaging model to represent long documents. P-SIF retains the simplicity of simple weighted word averaging while taking a document's thematic structure into account. In particular, P-SIF learns topic-specific vectors from a document and finally concatenates them all to represent the overall document. Through our experiments over multiple real-world datasets and tasks, we demonstrate PSIF's effectiveness compared to simple weighted averaging and many other state-of-the-art baselines. We also show that PSIF is particularly effective in representing long multi-sentence documents. We will release PSIF's embedding source code and data-sets for reproducing results.
Diving into Optimization of Topology in Neural Networks
Authors: Kun Yuan, Quanquan Li, Yucong Zhou, Jing Shao, Junjie Yan

Seeking effective networks has become one of the most crucial and practical areas in deep learning. The architecture of a neural network can be represented as a directed acyclic graph, whose nodes denote transformation of layers and edges represent information flow. Despite the selection of \textit{micro} node operations, \textit{macro} connections among the whole network, noted as \textit{topology}, largely affects the optimization process. We first rethink the residual connections via a new \textit{topological view} and observe the benefits provided by dense connections to the optimization. Motivated by which, we propose an innovation method to optimize the topology of a neural network. The optimization space is defined as a complete graph, through assigning learnable weights which reflect the importance of connections, the optimization of topology is transformed into learning a set of  continuous variables of edges. To extend the optimization to larger search spaces, a new series of networks,
named as TopoNet, are designed. To further focus on critical edges and promote generalization ablity in dense topologies, auxiliary sparsity constraint is adopted to constrain the distribution of edges. Experiments on classical networks prove the effectiveness of the optimization of topology. Experiments with TopoNets further verify both availability and transferability of the proposed method in
different tasks e.g. image classification, object detection and face recognition.
Point Process Flows
Authors: Nazanin Mehrasa, Ruizhi Deng, Mohamed Osama Ahmed, Bo Chang, Jiawei He, Thibaut Durand, Marcus Brubaker, Greg Mori

Event sequences can be modeled by temporal point processes (TPPs) to capture their asynchronous and probabilistic nature. We propose an intensity-free framework that directly models the point process as a non-parametric distribution by utilizing normalizing flows. This approach is capable of capturing highly complex temporal distributions and does not rely on restrictive parametric forms. Comparisons with state-of-the-art baseline models on both synthetic and challenging real-life datasets show that the proposed framework is effective at modeling the stochasticity of discrete event sequences.
Well-Read Students Learn Better: On the Importance of Pre-training Compact Models
Authors: Iulia Turc, Ming-Wei Chang, Kenton Lee, Kristina Toutanova

Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training. Due to the cost of applying such models to down-stream tasks, several model compression techniques on pre-trained language representations have been proposed (Sun et al., 2019; Sanh, 2019). However, surprisingly,  the simple baseline of just pre-training and fine-tuning compact models has been overlooked. In this paper, we first show that pre-training remains important in the context of smaller architectures, and fine-tuning pre-trained compact models can be competitive to more elaborate methods proposed in concurrent work. Starting with pre-trained compact models, we then explore transferring task knowledge from large fine-tuned models through standard knowledge distillation. The resulting simple, yet effective and general algorithm, Pre-trained Distillation, brings further improvements. Through extensive experiments, we more generally explore the interaction between pre-training and distillation under two variables that have been under-studied: model size and properties of unlabeled task data. One surprising observation is that they have a compound effect even when sequentially applied on the same data. To accelerate future research, we will make our 24 pre-trained miniature BERT models publicly available.
GraphCodeBERT: Pre-training Code Representations with Data Flow
Authors: Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie LIU, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou

Pre-trained models for programming language have achieved dramatic empirical improvements on a variety of code-related tasks such as code search, code completion, code summarization, etc. However, existing pre-trained models regard a code snippet as a sequence of tokens, while ignoring the inherent structure of code, which provides crucial code semantics and would enhance the code understanding process. We present GraphCodeBERT, a pre-trained model for programming language that considers the inherent structure of code. Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables. Such a semantic-level structure is neat and does not bring an unnecessarily deep hierarchy of AST, the property of which makes the model more efficient. We develop GraphCodeBERT based on Transformer. In addition to using the task of masked language modeling, we introduce two structure-aware pre-training tasks. One is to predict code structure edges, and the other is to align representations between source code and code structure. We implement the model in an efficient way with a graph-guided masked attention function to incorporate the code structure. We evaluate our model on four tasks, including code search, clone detection, code translation, and code refinement. Results show that code structure and newly introduced pre-training tasks can improve GraphCodeBERT and achieves state-of-the-art performance on the four downstream tasks. We further show that the model prefers structure-level attentions over token-level attentions in the task of code search.
MEAN-FIELD ANALYSIS OF BATCH NORMALIZATION
Authors: Mingwei Wei, James Stokes, David J Schwab

Batch Normalization (BatchNorm) is an extremely useful component of modern neural network architectures, enabling optimization using higher learning rates and achieving faster convergence. In this paper, we use mean-field theory to analytically quantify the impact of BatchNorm on the geometry of the loss landscape for multi-layer networks consisting of fully-connected and convolutional layers. We show that it has a flattening effect on the loss landscape, as quantified by the maximum eigenvalue of the Fisher Information Matrix. These findings are then used to justify the use of larger learning rates for networks that use BatchNorm, and we provide quantitative characterization of the maximal allowable learning rate to ensure convergence. Experiments support our theoretically predicted maximum learning rate, and furthermore suggest that networks with smaller values of the BatchNorm parameter achieve lower loss after the same number of epochs of training.
Learning concise representations for regression by evolving networks of trees
Authors: William La Cava, Tilak Raj Singh, James Taggart, Srinivas Suri, Jason H. Moore

We propose and study a method for learning interpretable representations for the task of regression. Features are represented as networks of multi-type expression trees comprised of activation functions common in neural networks in addition to other elementary functions. Differentiable features are trained via gradient descent, and the performance of features in a linear model is used to weight the rate of change among subcomponents of each representation. The search process maintains an archive of representations with accuracy-complexity trade-offs to assist in generalization and interpretation. We compare several stochastic optimization approaches within this framework. We benchmark these variants on 100 open-source regression problems in comparison to state-of-the-art machine learning approaches. Our main finding is that this approach produces the highest average test scores across problems while producing representations that are orders of magnitude smaller than the next best performing method (gradient boosting). We also report a negative result in which attempts to directly optimize the disentanglement of the representation result in more highly correlated features.
Continuous Transfer Learning
Authors: Jun Wu, Jingrui He

Transfer learning has been successfully applied across many high-impact applications. However, most existing work focuses on the static transfer learning setting, and very little is devoted to modeling the time evolving target domain, such as the online reviews for movies. To bridge this gap, in this paper, we focus on the continuous transfer learning setting with a time evolving target domain. One major challenge associated with continuous transfer learning is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time. To address this challenge, we first derive a generic generalization error bound on the current target domain with flexible domain discrepancy measures. Furthermore, a novel label-informed C-divergence is proposed to measure the shift of joint data distributions (over input features and output labels) across domains. It could be utilized to instantiate a tighter error upper bound in the continuous transfer learning setting, thus motivating us to develop an adversarial Variational Auto-encoder algorithm named CONTE by minimizing the C-divergence based error upper bound. Extensive experiments on various data sets demonstrate the effectiveness of our CONTE algorithm.
Learning Two-Time-Scale Representations For Large Scale Recommendations
Authors: Xinshi Chen, Yan Zhu, Haowen Xu, Muhan Zhang, Liang Xiong, Le Song

We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation. In our approach, we will partition users into two sets, active users with many observed interactions and inactive or new users with few observed interactions, and we will use two RNNs to model them separately. Furthermore, we design a two-stage training method for our model, where, in the first stage, we learn transductive embeddings for users and items, and then, in the second stage, we learn the two RNNs leveraging the transductive embeddings trained in the first stage. Through the lens of online learning and stochastic optimization, we provide theoretical analysis that motivates the design of our 2TS model. The 2TS model achieves a nice bias-variance trade-off while being computationally efficient. In large scale datasets, our 2TS model is able to achieve significantly better recommendations than previous state-of-the-art, yet being much more computationally efficient.
Can I Trust the Explainer? Verifying Post-Hoc Explanatory Methods
Authors: Oana-Maria Camburu*, Eleonora Giunchiglia*, Jakob Foerster, Thomas Lukasiewicz, Phil Blunsom

For AI systems to garner widespread public acceptance, we must develop methods capable of explaining the decisions of black-box models such as neural networks. In this work, we identify two issues of current explanatory methods. First, we show that two prevalent perspectives on explanations—feature-additivity and feature-selection—lead to fundamentally different instance-wise explanations. In the literature, explainers from different perspectives are currently being directly compared, despite their distinct explanation goals. The second issue is that current post-hoc explainers have only been thoroughly validated on simple models, such as linear regression, and, when applied to real-world neural networks, explainers are commonly evaluated under the assumption that the learned models behave reasonably. However, neural networks often rely on unreasonable correlations, even when producing correct decisions. We introduce a verification framework for explanatory methods under the feature-selection perspective. Our framework is based on a non-trivial neural network architecture trained on a real-world task, and for which we are able to provide guarantees on its inner workings. We validate the efficacy of our evaluation by showing the failure modes of current explainers. We aim for this framework to provide a publicly available,1 off-the-shelf evaluation when the feature-selection perspective on explanations is needed.
MergeBERT: Program Merge Conflict Resolution via Neural Transformers
Authors: Alexey Svyatkovskiy, Todd Mytkowicz, Negar Ghorbani, Sarah Fakhoury, Elizabeth A Dinella, Christian Bird, Neel Sundaresan, Shuvendu Lahiri

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. 
Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity.

In this paper, we introduce MergeBERT, a novel neural program merge framework based on the token-level three-way differencing and a transformer encoder model. Exploiting restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data.

Our model achieves 63--68\% accuracy of merge resolution synthesis, yielding nearly a 3$\times$ performance improvement over existing structured, and 2$\times$ improvement over neural program merge tools. Finally, we demonstrate that MergeBERT is sufficiently flexible to work with source code files in Java, JavaScript, TypeScript, and C\# programming languages, and can generalize zero-shot to unseen languages.
Continual Deep Learning by Functional Regularisation of Memorable Past
Authors: Pingbo Pan, Alexander Immer, Siddharth Swaroop, Runa Eschenhagen, Richard E Turner, Mohammad Emtiyaz Khan

Continually learning new skills without forgetting old ones is an important quality for an intelligent system, yet most deep learning methods suffer from catastrophic forgetting of the past. Recent works have addressed this by regularising the network weights, but it is challenging to identify weights crucial to avoid forgetting. A better approach is to directly regularise the network outputs at past inputs, e.g., by using Gaussian processes (GPs), but this is usually computationally challenging. In this paper, we propose a scalable functional-regularisation approach where we regularise only over a few memorable past examples that are crucial to avoid forgetting. Our key idea is to use a GP formulation of deep networks, enabling us to both identify the memorable past and regularise over them. Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation methods are naturally combined with memory-based methods.