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Title: Deep Representations for Time-varying Brain Datasets Abstract: Finding an appropriate representation of dynamic activities in the brain is crucial for many downstream applications. Due to its highly dynamic nature, temporally averaged fMRI (functional magnetic resonance imaging) can only provide a narrow view of... |
Title: A Natural Language Processing Pipeline for Detecting Informal Data References in Academic Literature Abstract: Discovering authoritative links between publications and the datasets that they use can be a labor-intensive process. We introduce a natural language processing pipeline that retrieves and reviews publi... |
Title: FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid? Abstract: The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based models (e.g., BERT) or their variants. Training ... |
Title: Throwing Away Data Improves Worst-Class Error in Imbalanced Classification Abstract: Class imbalances pervade classification problems, yet their treatment differs in theory and practice. On the one hand, learning theory instructs us that \emph{more data is better}, as sample size relates inversely to the average... |
Title: PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes Abstract: Autoencoders (AE) provide a useful method for nonlinear dimensionality reduction but are ill-suited for low data regimes. Conversely, Principal Component Analysis (PCA) is data-efficient but is limited to linear dimensi... |
Title: Learning multi-scale functional representations of proteins from single-cell microscopy data Abstract: Protein function is inherently linked to its localization within the cell, and fluorescent microscopy data is an indispensable resource for learning representations of proteins. Despite major developments in mo... |
Title: Semi-Supervised Clustering of Sparse Graphs: Crossing the Information-Theoretic Threshold Abstract: The stochastic block model is a canonical random graph model for clustering and community detection on network-structured data. Decades of extensive study on the problem have established many profound results, amo... |
Title: Compressing Deep Graph Neural Networks via Adversarial Knowledge Distillation Abstract: Deep graph neural networks (GNNs) have been shown to be expressive for modeling graph-structured data. Nevertheless, the over-stacked architecture of deep graph models makes it difficult to deploy and rapidly test on mobile o... |
Title: HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records Abstract: Burnout is a significant public health concern affecting nearly half of the healthcare workforce. This paper presents the first end-to-end deep learning framework for predicting physician burnout b... |
Title: High-Order Pooling for Graph Neural Networks with Tensor Decomposition Abstract: Graph Neural Networks (GNNs) are attracting growing attention due to their effectiveness and flexibility in modeling a variety of graph-structured data. Exiting GNN architectures usually adopt simple pooling operations (e.g., sum, a... |
Title: RCC-GAN: Regularized Compound Conditional GAN for Large-Scale Tabular Data Synthesis Abstract: This paper introduces a novel generative adversarial network (GAN) for synthesizing large-scale tabular databases which contain various features such as continuous, discrete, and binary. Technically, our GAN belongs to... |
Title: Functional Network: A Novel Framework for Interpretability of Deep Neural Networks Abstract: The layered structure of deep neural networks hinders the use of numerous analysis tools and thus the development of its interpretability. Inspired by the success of functional brain networks, we propose a novel framewor... |
Title: Randomly Initialized One-Layer Neural Networks Make Data Linearly Separable Abstract: Recently, neural networks have been shown to perform exceptionally well in transforming two arbitrary sets into two linearly separable sets. Doing this with a randomly initialized neural network is of immense interest because t... |
Title: Semi-Parametric Deep Neural Networks in Linear Time and Memory Abstract: Recent advances in deep learning have been driven by large-scale parametric models, which can be computationally expensive and lack interpretability. Semi-parametric methods query the training set at inference time and can be more compact, ... |
Title: Embedding Neighborhoods Simultaneously t-SNE (ENS-t-SNE) Abstract: We propose an algorithm for visualizing a dataset by embedding it in 3-dimensional Euclidean space based on various given distances between the same pairs of datapoints. Its aim is to find an Embedding which preserves Neighborhoods Simultaneously... |
Title: On the Role of Bidirectionality in Language Model Pre-Training Abstract: Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult. In this work, we focus on bidirectionali... |
Title: ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest Abstract: Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations ... |
Title: Soft-SVM Regression For Binary Classification Abstract: The binomial deviance and the SVM hinge loss functions are two of the most widely used loss functions in machine learning. While there are many similarities between them, they also have their own strengths when dealing with different types of data. In this ... |
Title: Towards a Defense against Backdoor Attacks in Continual Federated Learning Abstract: Backdoor attacks are a major concern in federated learning (FL) pipelines where training data is sourced from untrusted clients over long periods of time (i.e., continual learning). Preventing such attacks is difficult because d... |
Title: Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks Abstract: With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before. It is highly important to model flexible loads based on demand response. Therefor... |
Title: Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization Abstract: Adversarial training (AT) has proven to be one of the most effective ways to defend Deep Neural Networks (DNNs) against adversarial attacks. However, the phenomenon of robust overfitting, i.e., the robustness will dr... |
Title: BabyBear: Cheap inference triage for expensive language models Abstract: Transformer language models provide superior accuracy over previous models but they are computationally and environmentally expensive. Borrowing the concept of model cascading from computer vision, we introduce BabyBear, a framework for cas... |
Title: MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection Abstract: Organizations leverage anomaly and changepoint detection algorithms to detect changes in user behavior or service availability and performance. Many off-the-shelf detection algorithms, though effective, cannot r... |
Title: Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees Abstract: We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical ra... |
Title: Learning Context-Aware Service Representation for Service Recommendation in Workflow Composition Abstract: As increasingly more software services have been published onto the Internet, it remains a significant challenge to recommend suitable services to facilitate scientific workflow composition. This paper prop... |
Title: Constrained Monotonic Neural Networks Abstract: Deep neural networks are becoming increasingly popular in approximating arbitrary functions from noisy data. But wider adoption is being hindered by the need to explain such models and to impose additional constraints on them. Monotonicity constraint is one of the ... |
Title: Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning Abstract: Federated learning has allowed training of a global model by aggregating local models trained on local nodes. However, it still takes client-server model, which can be further distributed, fully decentralized, or even ... |
Title: Attributing AUC-ROC to Analyze Binary Classifier Performance Abstract: Area Under the Receiver Operating Characteristic Curve (AUC-ROC) is a popular evaluation metric for binary classifiers. In this paper, we discuss techniques to segment the AUC-ROC along human-interpretable dimensions. AUC-ROC is not an additi... |
Title: Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture Abstract: In this paper we show that feedforward neural networks corresponding to arbitrary directed acyclic graphs undergo transition to linearity as their "width" approaches infinity. The width of these general networks... |
Title: Quadratic models for understanding neural network dynamics Abstract: In this work, we propose using a quadratic model as a tool for understanding properties of wide neural networks in both optimization and generalization. We show analytically that certain deep learning phenomena such as the "catapult phase" from... |
Title: Accelerating Frank-Wolfe via Averaging Step Directions Abstract: The Frank-Wolfe method is a popular method in sparse constrained optimization, due to its fast per-iteration complexity. However, the tradeoff is that its worst case global convergence is comparatively slow, and importantly, is fundamentally slower... |
Title: G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection Abstract: Arbitrary-oriented object representations contain the oriented bounding box (OBB), quadrilateral bounding box (QBB), and point set (PointSet). Each representation encounters problems that correspond to its characteristics, such as t... |
Title: SepIt: Approaching a Single Channel Speech Separation Bound Abstract: We present an upper bound for the Single Channel Speech Separation task, which is based on an assumption regarding the nature of short segments of speech. Using the bound, we are able to show that while the recent methods have made significant... |
Title: NFL: Robust Learned Index via Distribution Transformation Abstract: Recent works on learned index open a new direction for the indexing field. The key insight of the learned index is to approximate the mapping between keys and positions with piece-wise linear functions. Such methods require partitioning key spac... |
Title: Learning to Assemble Geometric Shapes Abstract: Assembling parts into an object is a combinatorial problem that arises in a variety of contexts in the real world and involves numerous applications in science and engineering. Previous related work tackles limited cases with identical unit parts or jigsaw-style pa... |
Title: Penalized Proximal Policy Optimization for Safe Reinforcement Learning Abstract: Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still struggle for efficient policy updates with hard const... |
Title: Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free Abstract: Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a particular trigger. Several works attempt to detect whether a given... |
Title: Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization Abstract: We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acqu... |
Title: Diverse Lottery Tickets Boost Ensemble from a Single Pretrained Model Abstract: Ensembling is a popular method used to improve performance as a last resort. However, ensembling multiple models finetuned from a single pretrained model has been not very effective; this could be due to the lack of diversity among e... |
Title: CDFKD-MFS: Collaborative Data-free Knowledge Distillation via Multi-level Feature Sharing Abstract: Recently, the compression and deployment of powerful deep neural networks (DNNs) on resource-limited edge devices to provide intelligent services have become attractive tasks. Although knowledge distillation (KD) ... |
Title: Faithful Explanations for Deep Graph Models Abstract: This paper studies faithful explanations for Graph Neural Networks (GNNs). First, we provide a new and general method for formally characterizing the faithfulness of explanations for GNNs. It applies to existing explanation methods, including feature attribut... |
Title: Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning Abstract: Recently, deploying deep neural network (DNN) models via collaborative inference, which splits a pre-trained model into two parts and executes them on user equipment (UE) and edge server respectiv... |
Title: A Quadrature Rule combining Control Variates and Adaptive Importance Sampling Abstract: Driven by several successful applications such as in stochastic gradient descent or in Bayesian computation, control variates have become a major tool for Monte Carlo integration. However, standard methods do not allow the di... |
Title: Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs Abstract: We study for the first time uncertainty-aware modeling of continuous-time dynamics of interacting objects. We introduce a new model that decomposes independent dynamics of single objects accurately from their interactions. By empl... |
Title: An interpretation of the final fully connected layer Abstract: In recent years neural networks have achieved state-of-the-art accuracy for various tasks but the the interpretation of the generated outputs still remains difficult. In this work we attempt to provide a method to understand the learnt weights in the... |
Title: Physics-Embedded Neural Networks: $\boldsymbol{\mathrm{E}(n)}$-Equivariant Graph Neural PDE Solvers Abstract: Graph neural network (GNN) is a promising approach to learning and predicting physical phenomena described in boundary value problems, such as partial differential equations (PDEs) with boundary conditio... |
Title: Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision Abstract: Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU accelerators have been collectively constru... |
Title: An Adaptive Contrastive Learning Model for Spike Sorting Abstract: Brain-computer interfaces (BCIs), is ways for electronic devices to communicate directly with the brain. For most medical-type brain-computer interface tasks, the activity of multiple units of neurons or local field potentials is sufficient for d... |
Title: Large Language Models are Zero-Shot Reasoners Abstract: Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique fo... |
Title: Compression-aware Training of Neural Networks using Frank-Wolfe Abstract: Many existing Neural Network pruning approaches either rely on retraining to compensate for pruning-caused performance degradation or they induce strong biases to converge to a specific sparse solution throughout training. A third paradigm... |
Title: How Human is Human Evaluation? Improving the Gold Standard for NLG with Utility Theory Abstract: Human ratings are treated as the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotators, and then rank NLG systems by their average scores. However, ... |
Title: Pynblint: a Static Analyzer for Python Jupyter Notebooks Abstract: Jupyter Notebook is the tool of choice of many data scientists in the early stages of ML workflows. The notebook format, however, has been criticized for inducing bad programming practices; indeed, researchers have already shown that open-source ... |
Title: Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production Abstract: The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from ... |
Title: Causal Influences Decouple From Their Underlying Network Structure In Echo State Networks Abstract: Echo State Networks (ESN) are versatile recurrent neural network models in which the hidden layer remains unaltered during training. Interactions among nodes of this static backbone produce diverse representations... |
Title: 3D helical CT reconstruction with memory efficient invertible Learned Primal-Dual method Abstract: Helical acquisition geometry is the most common geometry used in computed tomography (CT) scanners for medical imaging. We adapt the invertible Learned Primal-Dual (iLPD) deep neural network architecture so that it... |
Title: Bandwidth Selection for Gaussian Kernel Ridge Regression via Jacobian Control Abstract: Most machine learning methods depend on the tuning of hyper-parameters. For kernel ridge regression (KRR) with the Gaussian kernel, the hyper-parameter is the bandwidth. The bandwidth specifies the length-scale of the kernel ... |
Title: The Data-Production Dispositif Abstract: Machine learning (ML) depends on data to train and verify models. Very often, organizations outsource processes related to data work (i.e., generating and annotating data and evaluating outputs) through business process outsourcing (BPO) companies and crowdsourcing platfo... |
Title: Theoretical Analysis of Primal-Dual Algorithm for Non-Convex Stochastic Decentralized Optimization Abstract: In recent years, decentralized learning has emerged as a powerful tool not only for large-scale machine learning, but also for preserving privacy. One of the key challenges in decentralized learning is th... |
Title: Realization Theory Of Recurrent Neural ODEs Using Polynomial System Embeddings Abstract: In this paper we show that neural ODE analogs of recurrent (ODE-RNN) and Long Short-Term Memory (ODE-LSTM) networks can be algorithmically embeddeded into the class of polynomial systems. This embedding preserves input-outpu... |
Title: Learning Stabilizing Policies in Stochastic Control Systems Abstract: In this work, we address the problem of learning provably stable neural network policies for stochastic control systems. While recent work has demonstrated the feasibility of certifying given policies using martingale theory, the problem of ho... |
Title: Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs Abstract: This work proposes an algorithm for fMRI data analysis for the classification of ADHD disorders. There have been several breakthroughs in the analysis of fMRI via 3D convolutional neural networks (CNNs). With these new ... |
Title: Deep Low-Density Separation for Semi-Supervised Classification Abstract: Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (SSL) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised ... |
Title: Quantum Kerr Learning Abstract: Quantum machine learning is a rapidly evolving area that could facilitate important applications for quantum computing and significantly impact data science. In our work, we argue that a single Kerr mode might provide some extra quantum enhancements when using quantum kernel metho... |
Title: Neur2SP: Neural Two-Stage Stochastic Programming Abstract: Stochastic programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely applied and studied class of stochastic programming models. Solving 2SPs exactly ... |
Title: Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents Abstract: In this paper, we explore the use of multi-agent deep learning as well as learning to cooperate principles to meet stringent service level agreements, in terms of throughput and end-to-end delay, for a set of classified network... |
Title: Naive Few-Shot Learning: Sequence Consistency Evaluation Abstract: Cognitive psychologists often use the term $\textit{fluid intelligence}$ to describe the ability of humans to solve novel tasks without any prior training. In contrast to humans, deep neural networks can perform cognitive tasks only after extensi... |
Title: Concurrent Credit Assignment for Data-efficient Reinforcement Learning Abstract: The capability to widely sample the state and action spaces is a key ingredient toward building effective reinforcement learning algorithms. The variational optimization principles exposed in this paper emphasize the importance of a... |
Title: PatchNR: Learning from Small Data by Patch Normalizing Flow Regularization Abstract: Learning neural networks using only a small amount of data is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a regularizer for the variational modeling of inverse prob... |
Title: Improving Human Image Synthesis with Residual Fast Fourier Transformation and Wasserstein Distance Abstract: With the rapid development of the Metaverse, virtual humans have emerged, and human image synthesis and editing techniques, such as pose transfer, have recently become popular. Most of the existing techni... |
Title: FedEntropy: Efficient Device Grouping for Federated Learning Using Maximum Entropy Judgment Abstract: Along with the popularity of Artificial Intelligence (AI) and Internet-of-Things (IoT), Federated Learning (FL) has attracted steadily increasing attentions as a promising distributed machine learning paradigm, ... |
Title: Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust Models Abstract: Deep Learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval and more. Many techniques have evolved over the past de... |
Title: On statistic alignment for domain adaptation in structural health monitoring Abstract: The practical application of structural health monitoring (SHM) is often limited by the availability of labelled data. Transfer learning - specifically in the form of domain adaptation (DA) - gives rise to the possibility of l... |
Title: Deep Reinforcement Learning for Multi-class Imbalanced Training Abstract: With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority cla... |
Title: Ensemble Multi-Relational Graph Neural Networks Abstract: It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is able ... |
Title: Optimality Conditions and Algorithms for Top-K Arm Identification Abstract: We consider the top-k arm identification problem for multi-armed bandits with rewards belonging to a one-parameter canonical exponential family. The objective is to select the set of k arms with the highest mean rewards by sequential all... |
Title: Bias Discovery in Machine Learning Models for Mental Health Abstract: Fairness and bias are crucial concepts in artificial intelligence, yet they are relatively ignored in machine learning applications in clinical psychiatry. We computed fairness metrics and present bias mitigation strategies using a model train... |
Title: DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks Abstract: Deep learning is attracting interest across a variety of domains, including natural language processing, speech recognition, and computer vision. However, model training is time-consuming and requires huge computational r... |
Title: Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width Abstract: Substantial work indicates that the dynamics of neural networks (NNs) is closely related to their initialization of parameters. Inspired by the phase diagram for two-layer ReLU NNs with infinite width (Luo et al., 2021), we mak... |
Title: KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation Abstract: Leveraging graphs on recommender systems has gained popularity with the development of graph representation learning (GRL). In particular, knowledge graph embedding (KGE) and graph neural networks (GNNs) are... |
Title: Federated singular value decomposition for high dimensional data Abstract: Federated learning (FL) is emerging as a privacy-aware alternative to classical cloud-based machine learning. In FL, the sensitive data remains in data silos and only aggregated parameters are exchanged. Hospitals and research institution... |
Title: Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Classification Abstract: Deep neural networks generally perform poorly with datasets that suffer from quantity imbalance and classification difficulty imbalance between different classes. In order to alleviate the problem of datas... |
Title: Inference of a Rumor's Source in the Independent Cascade Model Abstract: We consider the so-called Independent Cascade Model for rumor spreading or epidemic processes popularized by Kempe et al.\ [2003]. In this model, a small subset of nodes from a network are the source of a rumor. In discrete time steps, each... |
Title: Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks Abstract: The score-based query attacks (SQAs) pose practical threats to deep neural networks by crafting adversarial perturbations within dozens of queries, only using the model's output scores. Nonetheless, we note th... |
Title: One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks Abstract: Unlearnable examples (ULEs) aim to protect data from unauthorized usage for training DNNs. Error-minimizing noise, which is injected to clean data, is one of the most successful methods for preventing DNNs from giving correct predic... |
Title: Not too little, not too much: a theoretical analysis of graph (over)smoothing Abstract: We analyze graph smoothing with \emph{mean aggregation}, where each node successively receives the average of the features of its neighbors. Indeed, it has quickly been observed that Graph Neural Networks (GNNs), which genera... |
Title: D$^\text{2}$UF: Deep Coded Aperture Design and Unrolling Algorithm for Compressive Spectral Image Fusion Abstract: Compressive spectral imaging (CSI) has attracted significant attention since it employs synthetic apertures to codify spatial and spectral information, sensing only 2D projections of the 3D spectral... |
Title: Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums Abstract: Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees conver... |
Title: Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning Abstract: Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not naturally divided into discrete increments. Here we consider the problem o... |
Title: Learning for Expressive Task-Related Sentence Representations Abstract: NLP models learn sentence representations for downstream tasks by tuning a model which is pre-trained by masked language modeling. However, after tuning, the learned sentence representations may be skewed heavily toward label space and thus ... |
Title: Rethinking Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization Abstract: Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks such as image captioning and visual question answering (VQA).... |
Title: Forecasting Multilinear Data via Transform-Based Tensor Autoregression Abstract: In the era of big data, there is an increasing demand for new methods for analyzing and forecasting 2-dimensional data. The current research aims to accomplish these goals through the combination of time-series modeling and multilin... |
Title: Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model Abstract: Mobile sensing-based modeling of behavioral changes could predict an oncoming psychotic relapse in schizophrenia patients for timely interventions. Deep learning models could complement ex... |
Title: Gacs-Korner Common Information Variational Autoencoder Abstract: We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique to each. Our notion of common information is a variational relaxation... |
Title: EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling Abstract: This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories. MCMC trajectories of different lengths correspond to models with different purposes. Our exp... |
Title: Asynchronous Neural Networks for Learning in Graphs Abstract: This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs. Existing graph neural networks use the synchronous distributed computing model and aggregate their neighbors in each round, whi... |
Title: RevUp: Revise and Update Information Bottleneck for Event Representation Abstract: In machine learning, latent variables play a key role to capture the underlying structure of data, but they are often unsupervised. When we have side knowledge that already has high-level information about the input data, we can u... |
Title: Taming the sign problem of explicitly antisymmetrized neural networks via rough activation functions Abstract: Explicit antisymmetrization of a two-layer neural network is a potential candidate for a universal function approximator for generic antisymmetric functions, which are ubiquitous in quantum physics. How... |
Title: Interpretation Quality Score for Measuring the Quality of interpretability methods Abstract: Machine learning (ML) models have been applied to a wide range of natural language processing (NLP) tasks in recent years. In addition to making accurate decisions, the necessity of understanding how models make their de... |
Title: History Compression via Language Models in Reinforcement Learning Abstract: In a partially observable Markov decision process (POMDP), an agent typically uses a representation of the past to approximate the underlying MDP. We propose to utilize a frozen Pretrained Language Transformer (PLT) for history represent... |
Title: Policy Compliance Detection via Expression Tree Inference Abstract: Policy Compliance Detection (PCD) is a task we encounter when reasoning over texts, e.g. legal frameworks. Previous work to address PCD relies heavily on modeling the task as a special case of Recognizing Textual Entailment. Entailment is applic... |
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