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Title: RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy Abstract: The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered t... |
Title: Time-triggered Federated Learning over Wireless Networks Abstract: The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers a... |
Title: Federated Progressive Sparsification (Purge, Merge, Tune)+ Abstract: To improve federated training of neural networks, we develop FedSparsify, a sparsification strategy based on progressive weight magnitude pruning. Our method has several benefits. First, since the size of the network becomes increasingly smalle... |
Title: Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network Abstract: Exercise testing has been available for more than a half-century and is a remarkably versatile tool for diagnostic and prognostic information of patients for a range of diseases, especi... |
Title: neuro2vec: Masked Fourier Spectrum Prediction for Neurophysiological Representation Learning Abstract: Extensive data labeling on neurophysiological signals is often prohibitively expensive or impractical, as it may require particular infrastructure or domain expertise. To address the appetite for data of deep l... |
Title: Investigating the Optimal Neural Network Parameters for Decoding Abstract: Neural Networks have been proved to work as decoders in telecommunications, so the ways of making it efficient will be investigated in this thesis. The different parameters to maximize the Neural Network Decoder's efficiency will be inves... |
Title: Multi-task Deep Neural Networks for Massive MIMO CSI Feedback Abstract: Deep learning has been widely applied for the channel state information (CSI) feedback in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system. For the typical supervised training of the feedback model, the... |
Title: A review of Federated Learning in Intrusion Detection Systems for IoT Abstract: Intrusion detection systems are evolving into intelligent systems that perform data analysis searching for anomalies in their environment. The development of deep learning technologies opened the door to build more complex and effect... |
Title: Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GD Abstract: We provide sharp path-dependent generalization and excess risk guarantees for the full-batch Gradient Descent (GD) algorithm on smooth losses (possibly non-Lipschitz, possibly nonconvex), under an interpolation regime. At t... |
Title: Event Detection Explorer: An Interactive Tool for Event Detection Exploration Abstract: Event Detection (ED) is an important task in natural language processing. In the past few years, many datasets have been introduced for advancing ED machine learning models. However, most of these datasets are under-explored ... |
Title: Learning Value Functions from Undirected State-only Experience Abstract: This paper tackles the problem of learning value functions from undirected state-only experience (state transitions without action labels i.e. (s,s',r) tuples). We first theoretically characterize the applicability of Q-learning in this set... |
Title: Focal Sparse Convolutional Networks for 3D Object Detection Abstract: Non-uniformed 3D sparse data, e.g., point clouds or voxels in different spatial positions, make contribution to the task of 3D object detection in different ways. Existing basic components in sparse convolutional networks (Sparse CNNs) process... |
Title: Meta-free few-shot learning via representation learning with weight averaging Abstract: Recent studies on few-shot classification using transfer learning pose challenges to the effectiveness and efficiency of episodic meta-learning algorithms. Transfer learning approaches are a natural alternative, but they are ... |
Title: Coarse-to-fine Q-attention with Tree Expansion Abstract: Coarse-to-fine Q-attention enables sample-efficient robot manipulation by discretizing the translation space in a coarse-to-fine manner, where the resolution gradually increases at each layer in the hierarchy. Although effective, Q-attention suffers from "... |
Title: From One Hand to Multiple Hands: Imitation Learning for Dexterous Manipulation from Single-Camera Teleoperation Abstract: We propose to perform imitation learning for dexterous manipulation with multi-finger robot hand from human demonstrations, and transfer the policy to the real robot hand. We introduce a nove... |
Title: AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap Abstract: Artificial Intelligence (AI) has found its applications in a variety of environments ranging from data science to cybersecurity. AI helps break through the limitations of traditional algorithms and provides more efficient and fle... |
Title: One-shot Federated Learning without Server-side Training Abstract: Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection. Due to the high communication cost of traditional FL, one-shot federated learning is gaining popularity as a way to reduce c... |
Title: Enhancing Privacy against Inversion Attacks in Federated Learning by using Mixing Gradients Strategies Abstract: Federated learning reduces the risk of information leakage, but remains vulnerable to attacks. We investigate how several neural network design decisions can defend against gradients inversion attacks... |
Title: Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering Abstract: In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck principle to learn common information among... |
Title: Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production Abstract: In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP bin... |
Title: Application of WGAN-GP in recommendation and Questioning the relevance of GAN-based approaches Abstract: Many neural-based recommender systems were proposed in recent years and part of them used Generative Adversarial Networks (GAN) to model user-item interactions. However, the exploration of Wasserstein GAN wit... |
Title: Double Diffusion Maps and their Latent Harmonics for Scientific Computations in Latent Space Abstract: We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series dat... |
Title: Multi stain graph fusion for multimodal integration in pathology Abstract: In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary info... |
Title: Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages Abstract: Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem of abusive language sho... |
Title: An Empirical Study of the Occurrence of Heavy-Tails in Training a ReLU Gate Abstract: A particular direction of recent advance about stochastic deep-learning algorithms has been about uncovering a rather mysterious heavy-tailed nature of the stationary distribution of these algorithms, even when the data distrib... |
Title: SoFaiR: Single Shot Fair Representation Learning Abstract: To avoid discriminatory uses of their data, organizations can learn to map them into a representation that filters out information related to sensitive attributes. However, all existing methods in fair representation learning generate a fairness-informat... |
Title: Process Knowledge-infused Learning for Suicidality Assessment on Social Media Abstract: Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential... |
Title: Learning Eco-Driving Strategies at Signalized Intersections Abstract: Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce f... |
Title: Toward Policy Explanations for Multi-Agent Reinforcement Learning Abstract: Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving. Explaining agent decisions is crucial for improving s... |
Title: Novel Applications for VAE-based Anomaly Detection Systems Abstract: The recent rise in deep learning technologies fueled innovation and boosted scientific research. Their achievements enabled new research directions for deep generative modeling (DGM), an increasingly popular approach that can create novel and u... |
Title: RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning Abstract: Offline reinforcement learning (RL) aims to find near-optimal policies from logged data without further environment interaction. Model-based algorithms, which learn a model of the environment from the dataset and perform conservati... |
Title: Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models Abstract: Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier-Stokes an... |
Title: Surrogate Assisted Evolutionary Multi-objective Optimisation applied to a Pressure Swing Adsorption system Abstract: Chemical plant design and optimisation have proven challenging due to the complexity of these real-world systems. The resulting complexity translates into high computational costs for these system... |
Title: Protein 3D structure-based neural networks highly improve the accuracy in compound-protein binding affinity prediction Abstract: Theoretically, the accuracy of computational models in predicting compound-protein binding affinities (CPAs) could be improved by the introduction of protein 3D structure information. ... |
Title: hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification Abstract: Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most case... |
Title: Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks Abstract: Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weather modeling, he... |
Title: Zero-Touch Network on Industrial IoT: An End-to-End Machine Learning Approach Abstract: Industry 4.0-enabled smart factory is expected to realize the next revolution for manufacturers. Although artificial intelligence (AI) technologies have improved productivity, current use cases belong to small-scale and singl... |
Title: Rate-Constrained Remote Contextual Bandits Abstract: We consider a rate-constrained contextual multi-armed bandit (RC-CMAB) problem, in which a group of agents are solving the same contextual multi-armed bandit (CMAB) problem. However, the contexts are observed by a remotely connected entity, i.e., the decision-... |
Title: Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition Abstract: This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary repres... |
Title: Gaussian Kernel Variance For an Adaptive Learning Method on Signals Over Graphs Abstract: This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal values in a network using known nodal valu... |
Title: Meta-Learning Based Early Fault Detection for Rolling Bearings via Few-Shot Anomaly Detection Abstract: Early fault detection (EFD) of rolling bearings can recognize slight deviation of the health states and contribute to the stability of mechanical systems. In practice, very limited target bearing data are avai... |
Title: Generating Examples From CLI Usage: Can Transformers Help? Abstract: Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks. Relying on outdated documentation and examples can lead programs to fail or be less efficient... |
Title: Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems Abstract: Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, maki... |
Title: SCGC : Self-Supervised Contrastive Graph Clustering Abstract: Graph clustering discovers groups or communities within networks. Deep learning methods such as autoencoders (AE) extract effective clustering and downstream representations but cannot incorporate rich structural information. While Graph Neural Networ... |
Title: Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective Abstract: Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts hav... |
Title: Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems Abstract: Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes relational abstractions in conjunction with... |
Title: Adaptable Text Matching via Meta-Weight Regulator Abstract: Neural text matching models have been used in a range of applications such as question answering and natural language inference, and have yielded a good performance. However, these neural models are of a limited adaptability, resulting in a decline in p... |
Title: SVD Perspectives for Augmenting DeepONet Flexibility and Interpretability Abstract: Deep operator networks (DeepONets) are powerful architectures for fast and accurate emulation of complex dynamics. As their remarkable generalization capabilities are primarily enabled by their projection-based attribute, we inve... |
Title: The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification Abstract: We study a family of adversarial multiclass classification problems and provide equivalent reformulations in terms of: 1) a family of generalized barycenter problems introduced in the paper and 2) a family of multi... |
Title: Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning Abstract: Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms require models of identical architec... |
Title: Accelerated Continuous-Time Approximate Dynamic Programming via Data-Assisted Hybrid Control Abstract: We introduce a new closed-loop architecture for the online solution of approximate optimal control problems in the context of continuous-time systems. Specifically, we introduce the first algorithm that incorpo... |
Title: Data-based price discrimination: information theoretic limitations and a minimax optimal strategy Abstract: This paper studies the gap between the classical pricing theory and the data-based pricing theory. We focus on the problem of price discrimination with a continuum of buyer types based on a finite sample o... |
Title: Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training Abstract: Recently, much progress has been made for self-supervised action recognition. Most existing approaches emphasize the contrastive relations among videos, including appearance and motion ... |
Title: A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising Abstract: Recently, convolutional neural networks (CNNs) and attention mechanisms have been widely used in image denoising and achieved satisfactory performance. However, the previous works mostly use a single head to re... |
Title: DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games Abstract: This paper presents a personalized character recommendation system for Multiplayer Online Battle Arena (MOBA) games which are considered as one of the most popular online video game genres around the world... |
Title: Bounded Memory Adversarial Bandits with Composite Anonymous Delayed Feedback Abstract: We study the adversarial bandit problem with composite anonymous delayed feedback. In this setting, losses of an action are split into $d$ components, spreading over consecutive rounds after the action is chosen. And in each r... |
Title: An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context Abstract: As use of data driven technologies spreads, software engineers are more often faced with the task of solving a business problem using data-driven methods such as machine learning (ML) algorithms. Deployment of ... |
Title: Machines of finite depth: towards a formalization of neural networks Abstract: We provide a unifying framework where artificial neural networks and their architectures can be formally described as particular cases of a general mathematical construction--machines of finite depth. Unlike neural networks, machines ... |
Title: Learning Green's functions associated with parabolic partial differential equations Abstract: Given input-output pairs from a parabolic partial differential equation (PDE) in any spatial dimension $n\geq 1$, we derive the first theoretically rigorous scheme for learning the associated Green's function $G$. Until... |
Title: Supervised Contrastive CSI Representation Learning for Massive MIMO Positioning Abstract: Similarity metric is crucial for massive MIMO positioning utilizing channel state information~(CSI). In this letter, we propose a novel massive MIMO CSI similarity learning method via deep convolutional neural network~(DCNN... |
Title: GTNet: A Tree-Based Deep Graph Learning Architecture Abstract: We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the tree representation, messages propagate upward from the leaf nodes to... |
Title: When Performance is not Enough -- A Multidisciplinary View on Clinical Decision Support Abstract: Scientific publications about machine learning in healthcare are often about implementing novel methods and boosting the performance - at least from a computer science perspective. However, beyond such often short-l... |
Title: Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles Abstract: The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is ran... |
Title: Transfer Learning with Pre-trained Conditional Generative Models Abstract: Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods generally assume at least one of (i) source and target task label spaces must overlap, (ii) source datasets are available... |
Title: Learning to Parallelize in a Shared-Memory Environment with Transformers Abstract: In past years, the world has switched to many-core and multi-core shared memory architectures. As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes to software ap... |
Title: Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study Abstract: The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks. Despite the advances in RL, it is still not widely adopted in the industry due to the need for an expensive large amou... |
Title: Detecting Backdoor Poisoning Attacks on Deep Neural Networks by Heatmap Clustering Abstract: Predicitions made by neural networks can be fraudulently altered by so-called poisoning attacks. A special case are backdoor poisoning attacks. We study suitable detection methods and introduce a new method called Heatma... |
Title: Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study Abstract: This paper compares the performances of three supervised machine learning algorithms in terms of predictive ability and model interpretation on structured or tabular data. The algorithms considere... |
Title: LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning Abstract: Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages, i.e., selecting t... |
Title: Spending Privacy Budget Fairly and Wisely Abstract: Differentially private (DP) synthetic data generation is a practical method for improving access to data as a means to encourage productive partnerships. One issue inherent to DP is that the "privacy budget" is generally "spent" evenly across features in the da... |
Title: Epicardial Adipose Tissue Segmentation from CT Images with A Semi-3D Neural Network Abstract: Epicardial adipose tissue is a type of adipose tissue located between the heart wall and a protective layer around the heart called the pericardium. The volume and thickness of epicardial adipose tissue are linked to va... |
Title: Forecasting foreign exchange rates with regression networks tuned by Bayesian optimization Abstract: The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates. To address this problem, we introduce a regression network termed RegPred Net. The exchang... |
Title: Improving Feature Generalizability with Multitask Learning in Class Incremental Learning Abstract: Many deep learning applications, like keyword spotting, require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL). The major challenge in CIL is catastrophic for... |
Title: GypSum: Learning Hybrid Representations for Code Summarization Abstract: Code summarization with deep learning has been widely studied in recent years. Current deep learning models for code summarization generally follow the principle in neural machine translation and adopt the encoder-decoder framework, where t... |
Title: Topological Data Analysis for Anomaly Detection in Host-Based Logs Abstract: Topological Data Analysis (TDA) gives practioners the ability to analyse the global structure of cybersecurity data. We use TDA for anomaly detection in host-based logs collected with the open-source Logging Made Easy (LME) project. We ... |
Title: Trainable Compound Activation Functions for Machine Learning Abstract: Activation functions (AF) are necessary components of neural networks that allow approximation of functions, but AFs in current use are usually simple monotonically increasing functions. In this paper, we propose trainable compound AF (TCA) c... |
Title: Using the Projected Belief Network at High Dimensions Abstract: The projected belief network (PBN) is a layered generative network (LGN) with tractable likelihood function, and is based on a feed-forward neural network (FFNN). There are two versions of the PBN: stochastic and deterministic (D-PBN), and each has ... |
Title: A Bayesian Approach To Graph Partitioning Abstract: A new algorithm based on bayesian inference for learning local graph conductance based on Gaussian Process(GP) is given that uses advanced MCMC convergence ideas to create a scalable and fast algorithm for convergence to stationary distribution which is provide... |
Title: Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump Abstract: As the pump-and-dump schemes (P&Ds) proliferate in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance, to inform potentially susceptible investors before they become victims. In this pap... |
Title: NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks Abstract: In this paper we investigate the correlation between NFT valuations and various features from three primary categories: public market data, NFT metadata, and social trends data. |
Title: An Iterative Labeling Method for Annotating Fisheries Imagery Abstract: In this paper, we present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces. We ... |
Title: Learning to Transfer Role Assignment Across Team Sizes Abstract: Multi-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive exploration over the exponentially large state-action space. A powerfu... |
Title: On the Dynamics of Inference and Learning Abstract: Statistical Inference is the process of determining a probability distribution over the space of parameters of a model given a data set. As more data becomes available this probability distribution becomes updated via the application of Bayes' theorem. We prese... |
Title: Meshless method stencil evaluation with machine learning Abstract: Meshless methods are an active and modern branch of numerical analysis with many intriguing benefits. One of the main open research questions related to local meshless methods is how to select the best possible stencil - a collection of neighbour... |
Title: Unsupervised Learning of Unbiased Visual Representations Abstract: Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. This results in a poor generalization to unbiased datasets, as the predictions strongly rely on peripheral and confounding factor... |
Title: Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization Abstract: The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimi... |
Title: MAPLE-Edge: A Runtime Latency Predictor for Edge Devices Abstract: Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although recent research has proposed ways of quickly estimating latency on u... |
Title: Towards assessing agricultural land suitability with causal machine learning Abstract: Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change. Recent developments in the field of causal ... |
Title: Scalable particle-based alternatives to EM Abstract: Building on (Neal and Hinton, 1998), where the problem tackled by EM is recast as the optimization of a free energy functional on an infinite-dimensional space, we obtain three practical particle-based alternatives to EM applicable to broad classes of models. ... |
Title: Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems Abstract: While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plau... |
Title: Counterfactual harm Abstract: To act safely and ethically in the real world, agents must be able to reason about harm and avoid harmful actions. In this paper we develop the first statistical definition of harm and a framework for incorporating harm into algorithmic decisions. We argue that harm is fundamentally... |
Title: Ollivier-Ricci Curvature For Head Pose Estimation From a Single Image Abstract: Head pose estimation is a crucial challenge for many real-world applications, such as attention and human behavior analysis. This paper aims to estimate head pose from a single image by applying notions of network curvature. In the r... |
Title: NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue Abstract: We present NLU++, a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment ... |
Title: Binding Actions to Objects in World Models Abstract: We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to objects, soft attention and hard attention, which we evaluate in the context of str... |
Title: Dropout Inference with Non-Uniform Weight Scaling Abstract: Dropout as regularization has been used extensively to prevent overfitting for training neural networks. During training, units and their connections are randomly dropped, which could be considered as sampling many different submodels from the original ... |
Title: TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs Abstract: Computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived f... |
Title: Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning Abstract: Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an agent is provided with the exact goal they intend t... |
Title: Can deep learning match the efficiency of human visual long-term memory in storing object details? Abstract: Humans have a remarkably large capacity to store detailed visual information in long-term memory even after a single exposure, as demonstrated by classic experiments in psychology. For example, Standing (... |
Title: Treating Crowdsourcing as Examination: How to Score Tasks and Online Workers? Abstract: Crowdsourcing is an online outsourcing mode which can solve the current machine learning algorithm's urge need for massive labeled data. Requester posts tasks on crowdsourcing platforms, which employ online workers over the I... |
Title: Faster online calibration without randomization: interval forecasts and the power of two choices Abstract: We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as... |
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