nils-herrmann/clean_scirepeval huggingface dataset:
Learning a Static Analyzer: A Case Study on a Toy Language
Authors: Manzil Zaheer, Jean-Baptiste Tristan, Michael L. Wick, Guy L. Steele Jr.

Static analyzers are meta-programs that analyze programs to detect
  potential errors or collect information. For example, they are used
  as security tools to detect potential buffer overflows. Also, they
  are used by compilers to verify that a program is well-formed and
  collect information to generate better code. In this paper, we
  address the following question: can a static analyzer be learned
  from data? More specifically, can we use deep learning to learn a
  static analyzer without the need for complicated feature
  engineering? We show that long short-term memory networks are able
  to learn a basic static analyzer for a simple toy language. However,
  pre-existing approaches based on feature engineering, hidden Markov
  models, or basic recurrent neural networks fail on such a simple
  problem. Finally, we show how to make such a tool usable by
  employing a language model to help the programmer detect where the
  reported errors are located.
Are there any 'object detectors' in the hidden layers of CNNs trained to identify objects or scenes?
Authors: Ella M. Gale, Nicholas Martin, Ryan Blything, Anh Nguyen, Jeffrey S. Bowers

Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work.  But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learned and the functional relevance of these representations. In an attempt to better characterize object selectivity, we undertake a comparison of various selectivity measures on a large set of units in AlexNet, including localist selectivity, precision, class-conditional mean activity selectivity (CCMAS), network dissection, the human interpretation of activation maximization (AM) images, and standard signal-detection measures.  We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates. Indeed, the most selective units had a poor hit-rate or a high false-alarm rate (or both) in object classification, making them poor object detectors.  We fail to find any units that are even remotely as selective as the 'grandmother cell' units reported in recurrent neural networks. In order to generalize these results, we compared selectivity measures on a few units in VGG-16 and GoogLeNet trained on the ImageNet or Places-365 datasets that have been described as 'object detectors'. Again, we find poor hit-rates and high false-alarm rates for object classification.
Mixture Representation Learning with Coupled Autoencoding Agents
Authors: Yeganeh Marghi, Rohan Gala, Uygar Sümbül

Jointly identifying a mixture of discrete and continuous factors of variability can help unravel complex phenomena. We study this problem by proposing an unsupervised framework called coupled mixture VAE (cpl-mixVAE), which utilizes multiple interacting autoencoding agents. The individual agents operate on augmented copies of training samples to learn mixture representations, while being encouraged to reach consensus on the categorical assignments. We provide theoretical justification to motivate the use of a multi-agent framework, and formulate it as a variational inference problem. We benchmark our approach on MNIST and dSprites, achieving state-of-the-art categorical assignments while preserving interpretability of the continuous factors. We then demonstrate the utility of this approach in jointly identifying cell types and type-specific, activity-regulated genes for a single-cell gene expression dataset profiling over 100 cortical neuron types.
On Characterizing the Capacity of Neural Networks Using Algebraic Topology
Authors: William H. Guss, Ruslan Salakhutdinov

The learnability of different neural architectures can be characterized directly by computable measures of data complexity. In this paper, we reframe the problem of architecture selection as understanding how data determines the most expressive and generalizable architectures suited to that data, beyond inductive bias. After suggesting algebraic topology as a measure for data complexity, we show that the power of a network to express the topological complexity of a dataset in its decision boundary is a strictly limiting factor in its ability to generalize. We then provide the first empirical characterization of the topological capacity of neural networks. Our empirical analysis shows that at every level of dataset complexity, neural networks exhibit topological phase transitions and stratification. This observation allowed us to connect existing theory to empirically driven conjectures on the choice of architectures for a single hidden layer neural networks.
S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks
Authors: Shuai Li, Kui Jia

We present a formal measure-theoretical theory of neural networks (NN) built on {\it probability coupling theory}. Particularly, we present an algorithm framework, Hierarchical Measure Group and Approximate System (HMGAS), nicknamed S-System, of which NNs are special cases. In addition to many other results, the framework enables us to prove that 1) NNs implement {\it renormalization group (RG)} using information geometry, which points out that the large scale property to renormalize is dual Bregman divergence and completes the analog between NNs and RG; 2) and under a set of {\it realistic} boundedness and diversity conditions, for {\it large size nonlinear deep} NNs with a class of losses, including the hinge loss, all local minima are global minima with zero loss errors, using random matrix theory.
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
Authors: Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net are computed in a spatially coherent way, using a sequential gating-based mechanism that distributes contextual information across 2-D space. We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class. Furthermore, we demonstrate that SDN can be applied to large images by synthesizing samples of high quality and coherence. In a vanilla VAE setting, we find that a powerful SDN decoder also improves learning disentangled representations, indicating that neural architectures play an important role in this task. Our results suggest favoring spatial dependency over convolutional layers in various VAE settings. The accompanying source code is given at https://github.com/djordjemila/sdn.
Unified Recurrence Modeling for Video Action Anticipation
Authors: Tsung-Ming Tai, Giuseppe Fiameni, Cheng-Kuang Lee, Simon See, Oswald Lanz

Forecasting future events based on evidence of current conditions is an innate skill of human beings, and key for predicting the outcome of any decision making. In artificial vision for example, we would like to predict the next human action before it is actually performed, without observing the future video frames associated to it. Computer vision models for action anticipation are expected to collect the subtle evidence in the preamble of the target actions. In prior studies recurrence modeling often leads to better performance, and the strong temporal inference is assumed to be a key element for reasonable prediction. To this end, we propose a unified recurrence modeling for video action anticipation by generalizing the recurrence mechanism from sequence into graph representation via message passing. The information flow in space-time can be described by the interaction between vertices and edges, and the changes of vertices for each incoming frame reflects the underlying dynamics. Our model leverages self-attention for all building blocks in the graph modeling, and we introduce different edge learning strategies can be end-to-end optimized while updating the vertices. Our experimental results demonstrate that our modeling method is light-weight, efficient, and outperforms all previous works on the large-scale EPIC-Kitchen dataset.
Multi-scale Attributed Node Embedding
Authors: Benedek Rozemberczki, Carl Allen, Rik Sarkar

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE).  Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes. We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings. Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.
A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax
Authors: Fenfei Guo, Mohit Iyyer, Leah Findlater, Jordan Boyd-Graber

We present a differentiable multi-prototype word representation model that disentangles senses of polysemous words and produces meaningful sense-specific embeddings without external resources. It jointly learns how to disambiguate senses given local context and how to represent senses using hard attention. Unlike previous multi-prototype models, our model approximates discrete sense selection in a differentiable manner via a modified Gumbel softmax. We also propose a novel human evaluation task that quantitatively measures (1) how meaningful the learned sense groups are to humans and (2) how well the model is able to disambiguate senses given a context sentence. Our model outperforms competing approaches on both human evaluations and multiple word similarity tasks.
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward
Authors: FNU Hairi, Jia Liu, Songtao Lu

In this paper, we establish the first finite-time convergence result of the actor-critic algorithm for fully decentralized multi-agent reinforcement learning (MARL) problems with average reward. 
In this problem, a set of $N$ agents work cooperatively to maximize the global average reward through interacting with their neighbors over a communication network.
We consider a practical MARL setting, where the rewards and actions of each agent are only known to itself, and the knowledge of joint actions of the agents is not assumed. 
Toward this end, we propose a mini-batch Markovian sampled fully decentralized actor-critic algorithm and analyze its finite-time convergence and sample complexity.
We show that the sample complexity of this algorithm is $\mathcal{O}(N^{2}/\epsilon^{2}\log(N/\epsilon))$.
Interestingly, this sample complexity bound matches that of the state-of-the-art single-agent actor-critic algorithms for reinforcement learning.
Reinforcement Learning Based Asymmetrical DNN Modularization for Optimal Loading
Authors: Brijraj Singh, Yash Jain, Mayukh Das, Praveen Doreswamy Naidu

Latency of DNN (Deep Neural Network) based prediction is the summation of model loading latency and inference latency. Model loading latency affects the first response from the applications, whereas inference latency affects the subsequent responses. As model loading latency is directly proportional to the model size, this work aims at improving the response time of an intelligent app by reducing the loading latency. The speedup is gained by asymmetrically modularizing the given DNN model among several small child models and loading them in parallel. The decision about number of feasible child models and their corresponding split positions are taken care by reinforcement learning unit (RLU). RLU takes into account the available hardware resources on-device and provides the best splitting index $k$ and their positions  $\vec{p}$ specific to the DNN model and device, where  $\vec{p}=(p_1, p_2, ..., p_k)$ and $p_i$  is the end position of $i^{th}$  child: $M_i$. The proposed method has shown significant loading improvement (up to 7X) on popular DNNs, used for camera use-case. The proposed method can be used to speed up the app response. Along with that RLU driven approach facilitates for On-device personalization by separating one module only with trainable layers and loading that particular module while training on-device.
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Authors: Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, Tomas Pfister

Recent advances in semi-supervised learning (SSL) demonstrate that a combination of consistency regularization and pseudo-labeling can effectively improve image classification accuracy in the low-data regime. Compared to classification, semantic segmentation tasks require much more intensive labeling costs. Thus, these tasks greatly benefit from data-efficient training methods. However, structured outputs in segmentation render particular difficulties (e.g., designing pseudo-labeling and augmentation) to apply existing SSL strategies. To address this problem, we present a simple and novel re-design of pseudo-labeling to generate well-calibrated structured pseudo labels for training with unlabeled or weakly-labeled data. Our proposed pseudo-labeling strategy is network structure agnostic to apply in a one-stage consistency training framework. We demonstrate the effectiveness of the proposed pseudo-labeling strategy in both low-data and high-data regimes. Extensive experiments have validated that pseudo labels generated from wisely fusing diverse sources and strong data augmentation are crucial to consistency training for segmentation. The source code will be released.
DEEP DENSITY NETWORKS AND UNCERTAINTY IN RECOMMENDER SYSTEMS
Authors: Yoel Zeldes, Stavros Theodorakis, Efrat Solodnik, Aviv Rotman, Gil Chamiel, Dan Friedman

Building robust online content recommendation systems requires learning com- plex interactions between user preferences and content features. The field has evolved rapidly in recent years from traditional multi-arm bandit and collabora- tive filtering techniques, with new methods integrating Deep Learning models that enable to capture non-linear feature interactions. Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation. In this paper we provide a novel method, Deep Density Networks (DDN) which deconvolves measurement and data uncertainty and predicts probability densities of CTR, enabling us to perform more efficient exploration of the feature space. We show the usefulness of using DDN online in a real world content recommendation system that serves billions of recommendations per day, and present online and offline results to eval- uate the benefit of using DDN.
Test-Time Adaptation to Distribution Shifts by Confidence Maximization and Input Transformation
Authors: Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach, Evgeny Levinkov, Thomas Brox, Jan Hendrik Metzen

Deep neural networks often exhibit poor performance on data that is unlikely under the train-time data distribution, for instance data affected by corruptions. Previous works demonstrate that test-time adaptation to data shift, for instance using entropy minimization, effectively improves performance on such shifted distributions. This paper focuses on the fully test-time adaptation setting, where only unlabeled data from the target distribution is required. This allows adapting arbitrary pretrained networks. Specifically, we propose a novel loss that improves test-time adaptation by addressing both premature convergence and instability of entropy minimization. This is achieved by replacing the entropy by a non-saturating surrogate and adding a diversity regularizer based on batch-wise entropy maximization that prevents convergence to trivial collapsed solutions. Moreover, we propose to prepend an input transformation module to the network that can partially undo test-time distribution shifts. Surprisingly, this preprocessing can be learned solely using the fully test-time adaptation loss in an end-to-end fashion without any target domain labels or source domain data. We show that our approach outperforms previous work in improving the robustness of publicly available pretrained image classifiers to common corruptions on such challenging benchmarks as ImageNet-C.
Ecological Reinforcement Learning
Authors: John D. Co-Reyes, Suvansh Sanjeev, Glen Berseth, Abhishek Gupta, Sergey Levine

Reinforcement learning algorithms have been shown to effectively learn tasks in a variety of static, deterministic, and  simplistic environments, but their application to environments which are characteristic of dynamic lifelong settings encountered in the real world has been limited. Understanding the impact of specific environmental properties on the learning dynamics of reinforcement learning algorithms is important as we want to align the environments in which we develop our algorithms with the real world, and this is strongly coupled with the type of intelligence which can be learned. In this work, we study what we refer to as ecological reinforcement learning: the interaction between properties of the environment and the reinforcement learning agent. To this end, we introduce environments with characteristics that we argue better reflect natural environments: non-episodic learning, uninformative ``fundamental drive'' reward signals, and natural dynamics that cause the environment to change even when the agent fails to take intelligent actions. We show these factors can have a profound effect on the learning progress of reinforcement learning algorithms. Surprisingly, we find that these seemingly more challenging learning conditions can often make reinforcement learning agents learn more effectively. Through this study, we hope to shift the focus of the community towards learning in realistic, natural environments with dynamic elements.
Learning Similarity Metrics for Numerical Simulations
Authors: Georg Kohl, Kiwon Um, Nils Thuerey

We propose a novel approach to compute a stable and generalizing metric (LNSM) with convolutional neural networks (CNN) to compare field data from a variety of numerical simulation sources. Our method employs a Siamese network architecture that is motivated by the mathematical properties of a metric and is known to work well for finding similarities of other data modalities. We leverage a controllable data generation setup with partial differential equation (PDE) solvers to create increasingly different outputs from a reference simulation. In addition, the data generation allows for adjusting the difficulty of the resulting learning task. A central component of our learned metric is a specialized loss function, that introduces knowledge about the correlation between single data samples into the training process. To demonstrate that the proposed approach outperforms existing simple metrics for vector spaces and other learned, image based metrics we evaluate the different methods on a large range of test data. Additionally, we analyze generalization benefits of using the proposed correlation loss and the impact of an adjustable training data difficulty.
Learning De-biased Representations with Biased Representations
Authors: Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, Seong Joon Oh

Many machine learning algorithms are trained and evaluated by splitting data from a single source into training and test sets. While such focus on in-distribution learning scenarios has led interesting advances, it has not been able to tell if models are relying on dataset biases as shortcuts for successful prediction (e.g., using snow cues for recognising snowmobiles). Such biased models fail to generalise when the bias shifts to a different class. The cross-bias generalisation problem has been addressed by de-biasing training data through augmentation or re-sampling, which are often prohibitive due to the data collection cost (e.g., collecting images of snowmobile on a desert) and the difficulty of quantifying or expressing biases in the first place. In this work, we propose a novel framework to train a de-biased representation by encouraging it to be different from a set of representations that are biased by design. This tactic is feasible in many scenarios where it is much easier to define a set of biased representations than to define and quantify bias. Our experiments and analyses show that our method discourages models from taking bias shortcuts, resulting in improved performances on de-biased test data.
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
Authors: Yikang Shen, Yi Tay, Che Zheng, Dara Bahri, Donald Metzler, Aaron Courville

There are two major classes of natural language grammars --- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words. While previous unsupervised parsing methods mostly focus on only inducing one class of grammars, we introduce a novel model, StructFormer, that can induce dependency and constituency structure at the same time. To achieve this, we propose a new parsing framework that can jointly generates constituency tree and dependency graph. Then we integrate the induced dependency relations into transformer, in a differentiable manner, through a novel dependency-constrained self-attention mechanism. Experimental results show that our model can achieve strong results on unsupervised constituency parsing, unsupervised dependency parsing and masked language modeling at the same time.
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
Authors: Jiameng Fan, Wenchao Li

Deep reinforcement learning (DRL) agents are often sensitive to visual changes that were unseen in their training environments. To address this problem, we leverage the sequential nature of RL to learn robust representations that encode only task-relevant information from observations based on the unsupervised multi-view setting. Specifically, we introduce a novel contrastive version of Multi-View Information Bottleneck (MIB) objective for temporal data. We train RL agents from pixels with this auxiliary objective to learn robust representations that can compress away task-irrelevant information and are predictive of task-relevant dynamics. This approach enables us to train high-performance policies that are robust to visual distractions and can generalize well to unseen environments. We demonstrate that our approach can achieve SOTA performance on diverse visual control tasks on the DeepMind Control Suite when the background is replaced with natural videos. In addition, we show that our approach outperforms well-established baselines for generalization to unseen environments on the Procgen benchmark.