text stringlengths 0 4.09k |
|---|
Title: Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs Abstract: Dual embodied-symbolic concept representations are the foundation for deep learning and symbolic AI integration. We discuss the use of dual embodied-symbolic concept representations for molecular graph representation lear... |
Title: EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome Abstract: The ability to detect whether an object is a 2D or 3D object is extremely important in autonomous driving, since a detection error can have life-threatening consequences, endangering the safety of the driver, passengers... |
Title: A Study of the Attention Abnormality in Trojaned BERTs Abstract: Trojan attacks raise serious security concerns. In this paper, we investigate the underlying mechanism of Trojaned BERT models. We observe the attention focus drifting behavior of Trojaned models, i.e., when encountering an poisoned input, the trig... |
Title: Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks Abstract: This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression, characterized by their cross-sections and heights. These relations elucidate the... |
Title: Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning Abstract: Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas i... |
Title: Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks Abstract: Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. ... |
Title: Interlock-Free Multi-Aspect Rationalization for Text Classification Abstract: Explanation is important for text classification tasks. One prevalent type of explanation is rationales, which are text snippets of input text that suffice to yield the prediction and are meaningful to humans. A lot of research on rati... |
Title: Provably Safe Reinforcement Learning: A Theoretical and Experimental Comparison Abstract: Ensuring safety of reinforcement learning (RL) algorithms is crucial for many real-world tasks. However, vanilla RL does not guarantee safety for an agent. In recent years, several methods have been proposed to provide safe... |
Title: A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities Abstract: Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few ... |
Title: Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints Abstract: Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with in... |
Title: On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning Abstract: We study a pitfall in the typical workflow for differentially private machine learning. The use of differentially private learning algorithms in a "drop-in" fashion -- without accounting for the impact of... |
Title: A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data Abstract: A fundamental problem in the field of unsupervised machine learning is the detection of anomalies corresponding to rare and unusual observations of interest; reasons include for their rejection, accommodation or furth... |
Title: Leveraging Global Binary Masks for Structure Segmentation in Medical Images Abstract: Deep learning (DL) models for medical image segmentation are highly influenced by intensity variations of input images and lack generalization due to primarily utilizing pixels' intensity information for inference. Acquiring su... |
Title: Learning Keypoints from Synthetic Data for Robotic Cloth Folding Abstract: Robotic cloth manipulation is challenging due to its deformability, which makes determining its full state infeasible. However, for cloth folding, it suffices to know the position of a few semantic keypoints. Convolutional neural networks... |
Title: DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering Abstract: A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted Boltzmann Machine (DRBM) for processing unlabe... |
Title: Heavy-Tail Phenomenon in Decentralized SGD Abstract: Recent theoretical studies have shown that heavy-tails can emerge in stochastic optimization due to `multiplicative noise', even under surprisingly simple settings, such as linear regression with Gaussian data. While these studies have uncovered several intere... |
Title: Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction Abstract: Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond indivi... |
Title: The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials Abstract: The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic Cluster Expansion (ACE), which unified many of th... |
Title: Constructing Trajectory and Predicting Estimated Time of Arrival for Long Distance Travelling Vessels: A Probability Density-based Scanning Approach Abstract: In this study, a probability density-based approach for constructing trajectories is proposed and validated through an typical use-case application: Estim... |
Title: Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes Abstract: To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis. When it comes to analysis of bias, previous work has focused predominantly on race. In our work, we further investigate bias... |
Title: FastSTMF: Efficient tropical matrix factorization algorithm for sparse data Abstract: Matrix factorization, one of the most popular methods in machine learning, has recently benefited from introducing non-linearity in prediction tasks using tropical semiring. The non-linearity enables a better fit to extreme val... |
Title: The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation Abstract: Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference. The chose... |
Title: StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map Abstract: Despite recent success in conditional image synthesis, prevalent input conditions such as semantics and edges are not clear enough to express `Linear (Ridges)' and `Planar (Scale)' representations. To address this problem, we propos... |
Title: Improving Contextual Representation with Gloss Regularized Pre-training Abstract: Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of p... |
Title: Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets Abstract: Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for given inputs in a dataset do not... |
Title: The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 11 Abstract: Dyson spheres are hypothetical megastructures encircling stars in order to harvest most of their energy output. During the 11th edition of the GTOC challenge, participants were tasked with a complex trajectory planning relat... |
Title: Detecting Rumours with Latency Guarantees using Massive Streaming Data Abstract: Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, wh... |
Title: Convergence Analysis of Deep Residual Networks Abstract: Various powerful deep neural network architectures have made great contribution to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networks (ResNets) are of particular importance because they demonstrated great us... |
Title: Convergence of Deep Neural Networks with General Activation Functions and Pooling Abstract: Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the mathematical foundat... |
Title: Kronecker Decomposition for Knowledge Graph Embeddings Abstract: Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results indicate an ever increasing predictive ability of current approaches... |
Title: Uninorm-like parametric activation functions for human-understandable neural models Abstract: We present a deep learning model for finding human-understandable connections between input features. Our approach uses a parameterized, differentiable activation function, based on the theoretical background of nilpote... |
Title: Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest Abstract: Objective: During cardiac arrest treatment, a reliable detection of spontaneous circulation, usually performed by manual pulse checks, is both vital for patient survival and practically challenging. Methods: ... |
Title: Productivity Assessment of Neural Code Completion Abstract: Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows. Commercial products aim to increase programmers' productivity, without being able to measur... |
Title: Revisiting the Updates of a Pre-trained Model for Few-shot Learning Abstract: Most of the recent few-shot learning algorithms are based on transfer learning, where a model is pre-trained using a large amount of source data, and the pre-trained model is updated using a small amount of target data afterward. In tr... |
Title: Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation Abstract: Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementing spike sorting algorithms is burgeoning.... |
Title: Precise Change Point Detection using Spectral Drift Detection Abstract: The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and need adjustment. In this paper we consider the problem of detect... |
Title: Collaborative Drug Discovery: Inference-level Data Protection Perspective Abstract: Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform. On the other hand, there are non-negligible risks stemming from the unintended leakage of... |
Title: DualCF: Efficient Model Extraction Attack from Counterfactual Explanations Abstract: Cloud service providers have launched Machine-Learning-as-a-Service (MLaaS) platforms to allow users to access large-scale cloudbased models via APIs. In addition to prediction outputs, these APIs can also provide other informat... |
Title: Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems Abstract: Reinforcement learning (RL) is highly suitable for devising control strategies in the context of dynamical systems. A prominent instance of such a dynamical system is the system of equations governing fluid dynamics. Recent res... |
Title: A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification Abstract: Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a ... |
Title: OFedQIT: Communication-Efficient Online Federated Learning via Quantization and Intermittent Transmission Abstract: Online federated learning (OFL) is a promising framework to collaboratively learn a sequence of non-linear functions (or models) from distributed streaming data incoming to multiple clients while k... |
Title: Data-Driven Upper Bounds on Channel Capacity Abstract: We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet. A novel data-driven algorithm is proposed that exploits the dual representation of capacity where the maximi... |
Title: l-Leaks: Membership Inference Attacks with Logits Abstract: Machine Learning (ML) has made unprecedented progress in the past several decades. However, due to the memorability of the training data, ML is susceptible to various attacks, especially Membership Inference Attacks (MIAs), the objective of which is to ... |
Title: Modularity in NEAT Reinforcement Learning Networks Abstract: Modularity is essential to many well-performing structured systems, as it is a useful means of managing complexity [8]. An analysis of modularity in neural networks produced by machine learning algorithms can offer valuable insight into the workings of... |
Title: Test-time Fourier Style Calibration for Domain Generalization Abstract: The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods have achieved promising results, they primarily rely on the s... |
Title: Design and Implementation of a Quantum Kernel for Natural Language Processing Abstract: Natural language processing (NLP) is the field that attempts to make human language accessible to computers, and it relies on applying a mathematical model to express the meaning of symbolic language. One such model, DisCoCat... |
Title: Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review Abstract: Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of... |
Title: The Neuro-Symbolic Brain Abstract: Neural networks promote a distributed representation with no clear place for symbols. Despite this, we propose that symbols are manufactured simply by training a sparse random noise as a self-sustaining attractor in a feedback spiking neural network. This way, we can generate m... |
Title: Fast Conditional Network Compression Using Bayesian HyperNetworks Abstract: We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts, e.g. a context inv... |
Title: PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning Abstract: Contrastive learning pre-trains an image encoder using a large amount of unlabeled data such that the image encoder can be used as a general-purpose feature extractor for various downstream tasks. In this work, we propos... |
Title: $α$-GAN: Convergence and Estimation Guarantees Abstract: We prove a two-way correspondence between the min-max optimization of general CPE loss function GANs and the minimization of associated $f$-divergences. We then focus on $\alpha$-GAN, defined via the $\alpha$-loss, which interpolates several GANs (Hellinge... |
Title: KASAM: Spline Additive Models for Function Approximation Abstract: Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is introduced. Catastrophic forgetting can be alleviated by specifical... |
Title: How to Combine Membership-Inference Attacks on Multiple Updated Models Abstract: A large body of research has shown that machine learning models are vulnerable to membership inference (MI) attacks that violate the privacy of the participants in the training data. Most MI research focuses on the case of a single ... |
Title: Deep Learning for Prawn Farming: Forecasting and Anomaly Detection Abstract: We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parame... |
Title: Warm-starting DARTS using meta-learning Abstract: Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML). NAS has outperformed hand-designed networks and made a significant step forward in the field of automating the design of deep neural networks, thus furth... |
Title: Interpretable Climate Change Modeling With Progressive Cascade Networks Abstract: Typical deep learning approaches to modeling high-dimensional data often result in complex models that do not easily reveal a new understanding of the data. Research in the deep learning field is very actively pursuing new methods ... |
Title: Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning Abstract: We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). GWI leverages the Wasser... |
Title: Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations Abstract: Perceptual understanding of the scene and the relationship between its different components is important for successful completion of robotic tasks. Representation learning has been shown to be a powerful technique for this, b... |
Title: Collaborative Multi-agent Stochastic Linear Bandits Abstract: We study a collaborative multi-agent stochastic linear bandit setting, where $N$ agents that form a network communicate locally to minimize their overall regret. In this setting, each agent has its own linear bandit problem (its own reward parameter) ... |
Title: Multi-Environment Meta-Learning in Stochastic Linear Bandits Abstract: In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired by the work of [1] on meta-learning in a sequence of linear ba... |
Title: Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems Abstract: Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify pat... |
Title: Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines Abstract: Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence... |
Title: Global geomagnetic perturbation forecasting using Deep Learning Abstract: Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere, and drive catastrophic destruction to our technologically d... |
Title: Using Natural Sentences for Understanding Biases in Language Models Abstract: Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need for a prompt-style dataset to trigger specific behaviors of language models. In this paper, we addres... |
Title: Improving Sequential Query Recommendation with Immediate User Feedback Abstract: We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recommendation algorithms are based on sequence-to-sequen... |
Title: Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Recommendation Abstract: User evaluations include a significant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential to ... |
Title: Adaptive Block Floating-Point for Analog Deep Learning Hardware Abstract: Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts. However, recent studies show that DNNs on AMS devices with fixed-point numbers can incur an accura... |
Title: Topologically-Aware Deformation Fields for Single-View 3D Reconstruction Abstract: We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generated implicitly as deformations to a category-specifi... |
Title: ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training Abstract: Negative flips are errors introduced in a classification system when a legacy model is replaced with a new one. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy using model... |
Title: Evolving SimGANs to Improve Abnormal Electrocardiogram Classification Abstract: Machine Learning models are used in a wide variety of domains. However, machine learning methods often require a large amount of data in order to be successful. This is especially troublesome in domains where collecting real-world da... |
Title: SIBILA: High-performance computing and interpretable machine learning join efforts toward personalised medicine in a novel decision-making tool Abstract: Background and Objectives: Personalised medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made it a... |
Title: The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning Abstract: Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL) method by Grill et al. shows the negative term in contrastive loss can be removed if we add the so-called prediction head to the network. This initiated... |
Title: ScAN: Suicide Attempt and Ideation Events Dataset Abstract: Suicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors for death by suicide. Information related to patient... |
Title: Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets Abstract: This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications. The collection and annotation of such datasets are time-consuming and labor-int... |
Title: Exploiting symmetry in variational quantum machine learning Abstract: Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an ... |
Title: Contingency-constrained economic dispatch with safe reinforcement learning Abstract: Future power systems will rely heavily on micro grids with a high share of decentralised renewable energy sources and energy storage systems. The high complexity and uncertainty in this context might make conventional power disp... |
Title: kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval Abstract: Candidate generation is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in late... |
Title: Embodied vision for learning object representations Abstract: Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto close-by internal representations. When considering this learning approach... |
Title: Image Segmentation with Topological Priors Abstract: Solving segmentation tasks with topological priors proved to make fewer errors in fine-scale structures. In this work, we use topological priors both before and during the deep neural network training procedure. We compared the results of the two approaches wi... |
Title: A Generalist Agent Abstract: Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same netwo... |
Title: Mondrian Forest for Data Stream Classification Under Memory Constraints Abstract: Supervised learning algorithms generally assume the availability of enough memory to store their data model during the training and test phases. However, in the Internet of Things, this assumption is unrealistic when data comes in ... |
Title: Localized Vision-Language Matching for Open-vocabulary Object Detection Abstract: In this work, we propose an open-world object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classes. It is a two-stage training approach that first uses a... |
Title: Neural Network-based OFDM Receiver for Resource Constrained IoT Devices Abstract: Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transce... |
Title: Smooth-Reduce: Leveraging Patches for Improved Certified Robustness Abstract: Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require augmenting data with large amounts of noise, which leads t... |
Title: Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers Abstract: Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number and speed of room to room t... |
Title: Fair NLP Models with Differentially Private Text Encoders Abstract: Encoded text representations often capture sensitive attributes about individuals (e.g., race or gender), which raise privacy concerns and can make downstream models unfair to certain groups. In this work, we propose FEDERATE, an approach that c... |
Title: Framework for inferring empirical causal graphs from binary data to support multidimensional poverty analysis Abstract: Poverty is one of the fundamental issues that mankind faces. Multidimensional Poverty Index (MPI) is deployed for measuring poverty issues in a population beyond monetary. However, MPI cannot p... |
Title: Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements Abstract: Prediction of an individual's race and ethnicity plays an important role in social science and public health research. Examples include studies of racial disparity in health and voting.... |
Title: Sample Complexity Bounds for Robustly Learning Decision Lists against Evasion Attacks Abstract: A fundamental problem in adversarial machine learning is to quantify how much training data is needed in the presence of evasion attacks. In this paper we address this issue within the framework of PAC learning, focus... |
Title: One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code Abstract: People perceive the world with multiple senses (e.g., through hearing sounds, reading words and seeing objects). However, most existing AI systems only process an individual modality. This paper present... |
Title: Zero-shot Code-Mixed Offensive Span Identification through Rationale Extraction Abstract: This paper investigates the effectiveness of sentence-level transformers for zero-shot offensive span identification on a code-mixed Tamil dataset. More specifically, we evaluate rationale extraction methods of Local Interp... |
Title: Secure Aggregation for Federated Learning in Flower Abstract: Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the server. To prevent private information being inferred from local model... |
Title: Equivariant quantum circuits for learning on weighted graphs Abstract: Variational quantum algorithms are the leading candidate for near-term advantage on noisy quantum hardware. When training a parametrized quantum circuit to solve a specific task, the choice of ansatz is one of the most important factors that ... |
Title: Social learning via actions in bandit environments Abstract: I study a game of strategic exploration with private payoffs and public actions in a Bayesian bandit setting. In particular, I look at cascade equilibria, in which agents switch over time from the risky action to the riskless action only when they beco... |
Title: Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation Abstract: News recommendation for anonymous readers is a useful but challenging task for many news portals, where interactions between readers and articles are limited within a temporary login session. Previous works ... |
Title: Unified Source-Filter GAN with Harmonic-plus-Noise Source Excitation Generation Abstract: This paper introduces a unified source-filter network with a harmonic-plus-noise source excitation generation mechanism. In our previous work, we proposed unified Source-Filter GAN (uSFGAN) for developing a high-fidelity ne... |
Title: Low-variance estimation in the Plackett-Luce model via quasi-Monte Carlo sampling Abstract: The Plackett-Luce (PL) model is ubiquitous in learning-to-rank (LTR) because it provides a useful and intuitive probabilistic model for sampling ranked lists. Counterfactual offline evaluation and optimization of ranking ... |
Title: Learning Generalized Policies Without Supervision Using GNNs Abstract: We consider the problem of learning generalized policies for classical planning domains using graph neural networks from small instances represented in lifted STRIPS. The problem has been considered before but the proposed neural architecture... |
Title: Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training Abstract: Driving behavior monitoring plays a crucial role in managing road safety and decreasing the risk of traffic accidents. Driving behavior is affected by multiple factors like vehicle characteristics, ... |
Title: Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning Abstract: In this work, we investigate the properties of data that cause popular representation learning approaches to fail. In particular, we find that in environments where states do not significantly overlap, variation... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.