nils-herrmann/clean_scirepeval huggingface dataset:
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION
Authors: Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models’ 
 generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understand(NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, outperforming the human baseline by a decent margin (90.3 versus
89.8). The pre-trained DeBERTa models and the source code were released at: https://github.com/microsoft/DeBERTa.
DNN Representations as Codewords: Manipulating Statistical Properties via Penalty Regularization
Authors: Daeyoung Choi, Changho Shin, Hyunghun Cho, Wonjong Rhee

Performance of Deep Neural Network (DNN) heavily depends on the characteristics of hidden layer representations. Unlike the codewords of channel coding, however, the representations of learning cannot be directly designed or controlled. Therefore, we develop a family of penalty regularizers where each one aims to affect one of representation's statistical properties such as sparsity, variance, or covariance. The regularizers are extended to perform class-wise regularization, and the extension is found to provide an outstanding shaping capability. A variety of statistical properties are investigated for 10 different regularization strategies including dropout and batch normalization, and several interesting findings are reported. Using the family of regularizers, performance improvements are confirmed for MNIST, CIFAR-100, and CIFAR-10 classification problems. But more importantly, our results suggest that understanding how to manipulate statistical properties of representations can be an important step toward understanding DNN and that the role and effect of DNN regularizers need to be reconsidered.
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer
Authors: Qinbin Li, Bingsheng He, Dawn Song

Federated learning enables multiple parties to collaboratively learn a model without exchanging their local data. Currently, federated averaging (FedAvg) is the most widely used federated learning algorithm. However, FedAvg or its variants have obvious shortcomings. It can only be used to learn differentiable models and needs many communication rounds to converge. In this paper, we propose a novel federated learning algorithm FedKT that needs only a single communication round (i.e., round-optimal). With applying the knowledge transfer approach, our algorithm can be applied to any classification model. Moreover, we develop the differentially private versions of FedKT and theoretically analyze the privacy loss. The experiments show that our method can achieve close or better accuracy compared with the other state-of-the-art federated learning algorithms.
Testing Robustness Against Unforeseen Adversaries
Authors: Daniel Kang, Yi Sun, Dan Hendrycks, Tom B Brown, Jacob Steinhardt

Most existing adversarial defenses only measure robustness to $L_p$ adversarial attacks. Not only are adversaries unlikely to exclusively create small $L_p$ perturbations, adversaries are unlikely to remain fixed. Adversaries adapt and evolve their attacks; hence adversarial defenses must be robust to a broad range of unforeseen attacks. We address this discrepancy between research and reality by proposing a new evaluation framework called ImageNet-UA. Our framework enables the research community to test ImageNet model robustness against attacks not encountered during training. To create ImageNet-UA's diverse attack suite, we introduce a total of four novel adversarial attacks. We also demonstrate that, in comparison to ImageNet-UA, prevailing $L_\infty$ robustness assessments give a narrow account of adversarial robustness. By evaluating current defenses with ImageNet-UA, we find they provide little robustness to unforeseen attacks. We hope the greater variety and realism of ImageNet-UA enables development of more robust defenses which can generalize beyond attacks seen during training.
Compressed Sensing with Deep Image Prior and Learned Regularization
Authors: Dave Van Veen, Ajil Jalal, Mahdi Soltanolkotabi, Eric Price, Sriram Vishwanath, Alexandros G. Dimakis

We propose a novel method for compressed sensing recovery using
untrained deep generative models. Our method is based on the recently
proposed Deep Image Prior (DIP), wherein the convolutional weights of
the network are optimized to match the observed measurements. We show
that this approach can be applied to solve any differentiable linear inverse
problem, outperforming previous unlearned methods. Unlike various learned approaches based on generative models, our method does not require pre-training over large datasets. We further introduce a novel learned regularization technique, which incorporates prior information on the network weights. This reduces reconstruction error, especially for noisy measurements. Finally we prove that, using the DIP optimization approach, moderately overparameterized single-layer networks trained can perfectly fit any signal despite the nonconvex nature of the fitting problem. This theoretical result provides justification for early stopping.
Prototype memory and attention mechanisms for few shot image generation
Authors: Tianqin Li, Zijie Li, Andrew Luo, Harold Rockwell, Amir Barati Farimani, Tai Sing Lee

Recent discoveries indicate that the neural codes in the primary visual cortex (V1) of macaque monkeys are complex, diverse and sparse. This leads us to ponder the computational advantages and functional role of these “grandmother cells." Here, we propose that such cells can serve as prototype memory priors that bias and shape the distributed feature processing within the image generation process in the brain. These memory prototypes are learned by momentum online clustering and are utilized via a memory-based attention operation, which we define as Memory Concept Attention (MoCA). To test our proposal, we show in a few-shot image generation task, that having a prototype memory during attention can improve image synthesis quality, learn interpretable visual concept clusters, as well as improve the robustness of the model. Interestingly, we also find that our attentional memory mechanism can implicitly modify the horizontal connections by updating the transformation into the prototype embedding space for self-attention. Insofar as GANs can be seen as plausible models for reasoning about the top-down synthesis in the analysis-by-synthesis loop of the hierarchical visual cortex, our findings demonstrate a plausible computational role for these “prototype concept" neurons in visual processing in the brain.
Query Embedding on Hyper-Relational Knowledge Graphs
Authors: Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, Mikhail Galkin

Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. Existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often employ a hyper-relational modeling paradigm. In this paradigm, typed edges may have several key-value pairs known as qualifiers that provide fine-grained context for facts. In queries, this context modifies the meaning of relations, and usually reduces the answer set. Hyper-relational queries are often observed in real-world KG applications, and existing approaches for approximate query answering cannot make use of qualifier pairs. In this work, we bridge this gap and extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries. Building upon recent advancements in Graph Neural Networks and query embedding techniques, we study how to embed and answer hyper-relational conjunctive queries. Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
Non-Local Graph Neural Networks
Authors: Meng Liu, Zhengyang Wang, Shuiwang Ji

Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs. However, tasks on disassortative graphs usually require non-local aggregation. In addition, we find that local aggregation is even harmful for some disassortative graphs. In this work, we propose a simple yet effective non-local aggregation framework with an efficient attention-guided sorting for GNNs. Based on it, we develop various non-local GNNs. We perform thorough experiments to analyze disassortative graph datasets and evaluate our non-local GNNs. Experimental results demonstrate that our non-local GNNs significantly outperform previous state-of-the-art methods on six benchmark datasets of disassortative graphs, in terms of both model performance and efficiency.
Topological Experience Replay
Authors: Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal

State-of-the-art deep Q-learning methods update Q-values using state transition tuples sampled from the experience replay buffer. This strategy often randomly samples or prioritizes data sampling based on measures such as the temporal difference (TD) error. Such sampling strategies can be inefficient at learning Q-function since a state's correct Q-value preconditions on the accurate successor states' Q-value. Disregarding such a successor's value dependency leads to useless updates and even learning wrong values.
To expedite Q-learning, we maintain states' dependency by organizing the agent's experience into a graph. Each edge in the graph represents a transition between two connected states. We perform value backups via a breadth-first search that expands vertices in the graph starting from the set of terminal states successively moving backward. We empirically show that our method is substantially more data-efficient than several baselines on a diverse range of goal-reaching tasks. Notably, the proposed method also outperforms baselines that consume more batches of training experience.
Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent
Authors: Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu

Designing energy-efficient networks is of critical importance for enabling state-of-the-art deep learning in mobile and edge settings where the computation and energy budgets are highly limited. Recently, Wu et al. (2019) framed the search of efficient neural architectures into a continuous splitting process: it iteratively splits existing neurons into multiple off-springs to achieve progressive loss minimization,  thus finding novel architectures by gradually growing the neural network. However, this method was not specifically tailored for designing energy-efficient networks, and is computationally expensive on large-scale benchmarks.  In this work, we substantially improve Wu et al. (2019) in two significant ways:  1) we incorporate the energy cost of splitting different neurons to better guide the splitting process, thereby discovering more energy-efficient network architectures; 2) we substantially speed up the splitting process of Wu et al. (2019), which requires expensive eigen-decomposition, by proposing a highly scalable Rayleigh-quotient stochastic gradient algorithm.  Our fast algorithm allows us to reduce the computational cost of splitting to the same level of typical back-propagation updates and enables efficient implementation on GPU. Extensive empirical results show that our method can train highly accurate and energy-efficient networks on challenging datasets such as ImageNet,  improving a variety of baselines,  including the pruning-based methods and expert-designed architectures.
Invariant-equivariant representation learning for multi-class data
Authors: Ilya Feige

Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the information of the class from which the data belongs, and an equivariant representation that encodes the symmetry transformation defining the particular data point within the class manifold (equivariant in the sense that the representation varies naturally with symmetry transformations). This approach to representation learning is conceptually transparent, easy to implement, and in-principle generally applicable to any data comprised of discrete classes of continuous distributions (e.g. objects in images, topics in language, individuals in behavioural data). We demonstrate qualitatively compelling representation learning and competitive quantitative performance, in both supervised and semi-supervised settings, versus comparable modelling approaches in the literature with little fine tuning.
An Information Retrieval Approach for Finding Dependent Subspaces of Multiple Views
Authors: Ziyuan Lin, Jaakko Peltonen

Finding relationships between multiple views of data is essential both in exploratory analysis and as pre-processing for predictive tasks. A prominent approach is to apply variants of Canonical Correlation Analysis (CCA), a classical method seeking correlated components between views. The basic CCA is restricted to maximizing a simple dependency criterion, correlation, measured directly between data coordinates. We introduce a new method that finds dependent subspaces of views directly optimized for the data analysis task of neighbor retrieval between multiple views. We optimize mappings for each view such as linear transformations to maximize cross-view similarity between neighborhoods of data samples. The criterion arises directly from the well-defined retrieval task, detects nonlinear and local similarities, measures dependency of data relationships rather than only individual data coordinates, and is related to well understood measures of information retrieval quality. In experiments the proposed method outperforms alternatives in preserving cross-view neighborhood similarities, and yields insights into local dependencies between multiple views.
Few-Shot Attribute Learning
Authors: Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang, James Lucas, Jake Snell, Xaq Pitkow, Andreas S. Tolias, Richard Zemel

Semantic concepts are often defined by a combination of attributes. The use of attributes also facilitates learning of new concepts with zero or few examples. However, the zero-shot learning paradigm assumes that the set of attributes are known and fixed, which is a limitation if a test-time task depends on a previously irrelevant attribute. In this work we study rapid learning of attributes that are previously not labeled in the dataset. Compared to standard few-shot learning of semantic classes, learning new attributes imposes a stiffer challenge. We found that directly supervising the model with a set of training attributes does not generalize well on the test attributes, whereas self-supervised pre-training brings significant improvement. We further experimented with random splits of the attribute space and found that the predictability of attributes provides an informative estimate of a model's ability to generalize.
Negotiating Team Formation Using Deep Reinforcement Learning
Authors: Yoram Bachrach, Richard Everett, Edward Hughes, Angeliki Lazaridou, Joel Leibo, Marc Lanctot, Mike Johanson, Wojtek Czarnecki, Thore Graepel

When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach
Authors: Ze Ye, Tengfei Ma, Chien-Chun Ni, Kin Sum Liu, Jie Gao, Chao Chen

Graph neural networks (GNNs) rely heavily on the underlying graph topology and thus can be vulnerable to malicious attacks targeting at graph structures. We propose a novel GNN defense algorithm against structural attacks that maliciously modify graph topology. In particular, we discover a robust representation of the input graph based on the advanced theory of graph Ricci flow, which captures the intrinsic geometry of graphs and is robust to structural perturbation. We propose an algorithm to train GNNs using re-sampled graphs based on such geometric representation. We show that this method can be effective to protect against adversarial structural attacks. Our method achieves state-of-the-art performance on synthetic and real datasets against different types of graph poisoning attacks.
AIM: Adversarial Inference by Matching Priors and Conditionals
Authors: Hanbo Li, Yaqing Wang, Changyou Chen, Jing Gao

Effective inference for a generative adversarial model remains an important and challenging problem. We propose a novel approach, Adversarial Inference by Matching priors and conditionals (AIM), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model. We derive an equivalent form of the prior and conditional matching objective that can be optimized efficiently without any parametric assumption on the data. We validate the effectiveness of AIM on the MNIST, CIFAR-10, and CelebA datasets by conducting quantitative and qualitative evaluations. Results demonstrate that AIM significantly improves both reconstruction and generation as compared to other adversarial inference models.
Learning a Non-Redundant Collection of Classifiers
Authors: Daniel Pace, Alessandra Russo, Murray Shanahan

Supervised learning models constructed under the i.i.d. assumption have often been shown to exploit spurious or brittle predictive signals instead of more robust ones present in the training data. Inspired by Quality-Diversity algorithms, in this work we train a collection of classifiers to learn distinct solutions to a classification problem, with the goal of learning to exploit a variety of predictive signals present in the training data. We propose an information-theoretic measure of model diversity based on minimizing an estimate of conditional total correlation of final layer representations across models given the label. We consider datasets with synthetically injected spurious correlations and evaluate our framework's ability to rapidly adapt to a change in distribution that destroys the spurious correlation. We compare our method to a variety of baselines under this evaluation protocol, showing that it is competitive with other approaches while being more successful at isolating distinct signals. We also show that our model is competitive with Invariant Risk Minimization under this evaluation protocol without requiring access to the environment information required by IRM to discriminate between spurious and robust signals.
On Margin Maximization in Linear and ReLU Networks
Authors: Gal Vardi, Ohad Shamir, Nathan Srebro

The implicit bias of neural networks has been extensively studied in recent years. Lyu and Li [2019] showed that in homogeneous networks trained with the exponential or the logistic loss, gradient flow converges to a KKT point of the max margin problem in the parameter space. However, that leaves open the question of whether this point will generally be an actual optimum of the max margin problem. In this paper, we study this question in detail, for several neural network architectures involving linear and ReLU activations. Perhaps surprisingly, we show that in many cases, the KKT point is not even a local optimum of the max margin problem. On the flip side, we identify multiple settings where a local or global optimum can be guaranteed. Finally, we answer a question posed in Lyu and Li [2019] by showing that for non-homogeneous networks, the normalized margin may strictly decrease over time.
Revisiting Reweighted Wake-Sleep
Authors: Tuan Anh Le, Adam R. Kosiorek, N. Siddharth, Yee Whye Teh, Frank Wood

Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn. Sampling discrete latent variables can result in high-variance gradient estimators for two primary reasons: 1) branching on the samples within the model, and 2) the lack of a pathwise derivative for the samples. While current state-of-the-art methods employ control-variate schemes for the former and continuous-relaxation methods for the latter, their utility is limited by the complexities of implementing and training effective control-variate schemes and the necessity of evaluating (potentially exponentially) many branch paths in the model. Here, we revisit the Reweighted Wake Sleep (RWS; Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it circumvents both these issues, outperforming current state-of-the-art methods in learning discrete latent-variable models. Moreover, we observe that, unlike the Importance-weighted Autoencoder, RWS learns better models and inference networks with increasing numbers of particles, and that its benefits extend to continuous latent-variable models as well. Our results suggest that RWS is a competitive, often preferable, alternative for learning deep generative models.