text stringlengths 0 4.09k |
|---|
Title: Sufficient Statistic Memory Approximate Message Passing Abstract: Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems. A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolut... |
Title: Backward baselines: Is your model predicting the past? Abstract: When does a machine learning model predict the future of individuals and when does it recite patterns that predate the individuals? In this work, we propose a distinction between these two pathways of prediction, supported by theoretical, empirical... |
Title: Adversarial Zoom Lens: A Novel Physical-World Attack to DNNs Abstract: Although deep neural networks (DNNs) are known to be fragile, no one has studied the effects of zooming-in and zooming-out of images in the physical world on DNNs performance. In this paper, we demonstrate a novel physical adversarial attack ... |
Title: Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty Abstract: Using Machine Learning systems in the real world can often be problematic, with inexplicable black-box models, the assumed certainty of imperfect measurements, or providing a single classification instead of a probability ... |
Title: Optimizing Two-way Partial AUC with an End-to-end Framework Abstract: The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifie... |
Title: Invariant Causal Mechanisms through Distribution Matching Abstract: Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks. One key property for robustness which the learned representation should capture and which recentl... |
Title: Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering Abstract: The development of precision agriculture has gradually introduced automation in the agricultural process to support and rationalize all the activities related to field management. In particular, service robotics plays ... |
Title: Improving decision-making via risk-based active learning: Probabilistic discriminative classifiers Abstract: Gaining the ability to make informed decisions on operation and maintenance of structures provides motivation for the implementation of structural health monitoring (SHM) systems. However, descriptive lab... |
Title: Prototype-Anchored Learning for Learning with Imperfect Annotations Abstract: The success of deep neural networks greatly relies on the availability of large amounts of high-quality annotated data, which however are difficult or expensive to obtain. The resulting labels may be class imbalanced, noisy or human bi... |
Title: Disentangling representations in Restricted Boltzmann Machines without adversaries Abstract: A goal of unsupervised machine learning is to disentangle representations of complex high-dimensional data, allowing for interpreting the significant latent factors of variation in the data as well as for manipulating th... |
Title: Learning Towards the Largest Margins Abstract: One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power. The classical softmax loss does not explicitly encourage discriminative learning of feat... |
Title: Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept Abstract: Nowadays cross-industry ranging challenges include the reduction of greenhouse gas emission and enabling a circular economy. However, the production of pap... |
Title: Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations Abstract: Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future pr... |
Title: Few-Shot Non-Parametric Learning with Deep Latent Variable Model Abstract: Most real-world problems that machine learning algorithms are expected to solve face the situation with 1) unknown data distribution; 2) little domain-specific knowledge; and 3) datasets with limited annotation. We propose Non-Parametric ... |
Title: LED: Latent Variable-based Estimation of Density Abstract: Modern generative models are roughly divided into two main categories: (1) models that can produce high-quality random samples, but cannot estimate the exact density of new data points and (2) those that provide exact density estimation, at the expense o... |
Title: A Geometric Method for Improved Uncertainty Estimation in Real-time Abstract: Machine learning classifiers are probabilistic in nature, and thus inevitably involve uncertainty. Predicting the probability of a specific input to be correct is called uncertainty (or confidence) estimation and is crucial for risk ma... |
Title: A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method Abstract: Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction. The obtained airfoils geometrical features in most existing app... |
Title: Rethinking Collaborative Metric Learning: Toward an Efficient Alternative without Negative Sampling Abstract: The recently proposed Collaborative Metric Learning (CML) paradigm has aroused wide interest in the area of recommendation systems (RS) owing to its simplicity and effectiveness. Typically, the existing ... |
Title: Stochastic Langevin Differential Inclusions with Applications to Machine Learning Abstract: Stochastic differential equations of Langevin-diffusion form have received significant recent, thanks to their foundational role in both Bayesian sampling algorithms and optimization in machine learning. In the latter, th... |
Title: Explanatory causal effects for model agnostic explanations Abstract: This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model. The causal effect of a feature (variable) on... |
Title: Low-Rank Mirror-Prox for Nonsmooth and Low-Rank Matrix Optimization Problems Abstract: Low-rank and nonsmooth matrix optimization problems capture many fundamental tasks in statistics and machine learning. While significant progress has been made in recent years in developing efficient methods for \textit{smooth... |
Title: Utilizing Expert Features for Contrastive Learning of Time-Series Representations Abstract: We present an approach that incorporates expert knowledge for time-series representation learning. Our method employs expert features to replace the commonly used data transformations in previous contrastive learning appr... |
Title: Quantum Approximation of Normalized Schatten Norms and Applications to Learning Abstract: Efficient measures to determine similarity of quantum states, such as the fidelity metric, have been widely studied. In this paper, we address the problem of defining a similarity measure for quantum operations that can be ... |
Title: CGAR: Critic Guided Action Redistribution in Reinforcement Leaning Abstract: Training a game-playing reinforcement learning agent requires multiple interactions with the environment. Ignorant random exploration may cause a waste of time and resources. It's essential to alleviate such waste. As discussed in this ... |
Title: Gradual Domain Adaptation via Normalizing Flows Abstract: Conventional domain adaptation methods do not work well when a large gap exists between the source and the target domain. Gradual domain adaptation is one of the approaches to address the problem by leveraging the intermediate domain, which gradually shif... |
Title: Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation Abstract: We study reinforcement learning with linear function approximation where the transition probability and reward functions are linear with respect to a feature mapping $\boldsymbol{\phi}(s,a)$. Specifically, we consider the ... |
Title: On Pre-Training for Federated Learning Abstract: In most of the literature on federated learning (FL), neural networks are initialized with random weights. In this paper, we present an empirical study on the effect of pre-training on FL. Specifically, we aim to investigate if pre-training can alleviate the drast... |
Title: Patient Aware Active Learning for Fine-Grained OCT Classification Abstract: This paper considers making active learning more sensible from a medical perspective. In practice, a disease manifests itself in different forms across patient cohorts. Existing frameworks have primarily used mathematical constructs to e... |
Title: A Framework for Understanding Model Extraction Attack and Defense Abstract: The privacy of machine learning models has become a significant concern in many emerging Machine-Learning-as-a-Service applications, where prediction services based on well-trained models are offered to users via pay-per-query. The lack ... |
Title: RetroGraph: Retrosynthetic Planning with Graph Search Abstract: Retrosynthetic planning, which aims to find a reaction pathway to synthesize a target molecule, plays an important role in chemistry and drug discovery. This task is usually modeled as a search problem. Recently, data-driven methods have attracted m... |
Title: Predicting the Geoeffectiveness of CMEs Using Machine Learning Abstract: Coronal mass ejections (CMEs) are the most geoeffective space weather phenomena, being associated with large geomagnetic storms, having the potential to cause disturbances to telecommunication, satellite network disruptions, power grid dama... |
Title: Modular Conformal Calibration Abstract: Uncertainty estimates must be calibrated (i.e., accurate) and sharp (i.e., informative) in order to be useful. This has motivated a variety of methods for recalibration, which use held-out data to turn an uncalibrated model into a calibrated model. However, the applicabili... |
Title: InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle Abstract: Adversarial training (AT) has shown excellent high performance in defending against adversarial examples. Recent studies demonstrate that examples are not equally important to the final robustness of models during AT, tha... |
Title: Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks Abstract: In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low co... |
Title: pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models Abstract: Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time so as to make predictions on their future interaction performance. Recently, remarkable ... |
Title: Efficient Adaptive Federated Optimization of Federated Learning for IoT Abstract: The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. The classical art... |
Title: Context matters for fairness -- a case study on the effect of spatial distribution shifts Abstract: With the ever growing involvement of data-driven AI-based decision making technologies in our daily social lives, the fairness of these systems is becoming a crucial phenomenon. However, an important and often cha... |
Title: Shilling Black-box Recommender Systems by Learning to Generate Fake User Profiles Abstract: Due to the pivotal role of Recommender Systems (RS) in guiding customers towards the purchase, there is a natural motivation for unscrupulous parties to spoof RS for profits. In this paper, we study Shilling Attack where ... |
Title: Recursive Reinforcement Learning Abstract: Recursion is the fundamental paradigm to finitely describe potentially infinite objects. As state-of-the-art reinforcement learning (RL) algorithms cannot directly reason about recursion, they must rely on the practitioner's ingenuity in designing a suitable "flat" repr... |
Title: On a class of geodesically convex optimization problems solved via Euclidean MM methods Abstract: We study geodesically convex (g-convex) problems that can be written as a difference of Euclidean convex functions. This structure arises in several optimization problems in statistics and machine learning, e.g., fo... |
Title: Functional Nonlinear Learning Abstract: Using representations of functional data can be more convenient and beneficial in subsequent statistical models than direct observations. These representations, in a lower-dimensional space, extract and compress information from individual curves. The existing representati... |
Title: Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing Abstract: Only recently, researchers attempt to provide classification algorithms with provable group fairness guarantees. Most of these algorithms suffer from harassment caused by the requirement that the training and deployment data follo... |
Title: FINGER: Fast Inference for Graph-based Approximate Nearest Neighbor Search Abstract: Approximate K-Nearest Neighbor Search (AKNNS) has now become ubiquitous in modern applications, for example, as a fast search procedure with two tower deep learning models. Graph-based methods for AKNNS in particular have receiv... |
Title: The ArtBench Dataset: Benchmarking Generative Models with Artworks Abstract: We introduce ArtBench-10, the first class-balanced, high-quality, cleanly annotated, and standardized dataset for benchmarking artwork generation. It comprises 60,000 images of artwork from 10 distinctive artistic styles, with 5,000 tra... |
Title: Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation Abstract: It has been a long-standing dream to design artificial agents that explore their environment efficiently via intrinsic motivation, similar to how children perform curious free play. Despite recent advances in intrinsic... |
Title: Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan Abstract: Can mobile phone data improve program targeting? By combining rich survey data from a "big push" anti-poverty program in Afghanistan with detailed mobile phone logs from program bene... |
Title: Learning Representations for Control with Hierarchical Forward Models Abstract: Learning control from pixels is difficult for reinforcement learning (RL) agents because representation learning and policy learning are intertwined. Previous approaches remedy this issue with auxiliary representation learning tasks,... |
Title: Bi-stochastically normalized graph Laplacian: convergence to manifold Laplacian and robustness to outlier noise Abstract: Bi-stochastic normalization of kernelized graph affinity matrix provides an alternative normalization scheme for graph Laplacian methods in graph-based data analysis and can be computed effic... |
Title: GACT: Activation Compressed Training for General Architectures Abstract: Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint. This paper presents GACT, an ACT framework to support a bro... |
Title: Projection-free Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data Abstract: We study a projection-free conditional gradient-type algorithm for constrained nonconvex stochastic optimization problems with Markovian data. In particular, we focus on the case when the transition kernel of... |
Title: Synthetic Data-Based Simulators for Recommender Systems: A Survey Abstract: This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation (M&S) of interactions between users and recommender systems and applications of the M&S to the performance improvement of... |
Title: Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer Abstract: In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are expressed linearly, and the agent has... |
Title: Community Recovery in the Geometric Block Model Abstract: To capture inherent geometric features of many community detection problems, we propose to use a new random graph model of communities that we call a \emph{Geometric Block Model}. The geometric block model builds on the \emph{random geometric graphs} (Gil... |
Title: Latent Policies for Adversarial Imitation Learning Abstract: This paper considers learning robot locomotion and manipulation tasks from expert demonstrations. Generative adversarial imitation learning (GAIL) trains a discriminator that distinguishes expert from agent transitions, and in turn use a reward defined... |
Title: Neural Implicit Manifold Learning for Topology-Aware Generative Modelling Abstract: Natural data observed in $\mathbb{R}^n$ is often constrained to an $m$-dimensional manifold $\mathcal{M}$, where $m < n$. Current generative models represent this manifold by mapping an $m$-dimensional latent variable through a n... |
Title: Langevin Monte Carlo for Contextual Bandits Abstract: We study the efficiency of Thompson sampling for contextual bandits. Existing Thompson sampling-based algorithms need to construct a Laplace approximation (i.e., a Gaussian distribution) of the posterior distribution, which is inefficient to sample in high di... |
Title: Behavior Transformers: Cloning $k$ modes with one stone Abstract: While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large, human-generated datasets. Human behaviors have wide variance, multiple mod... |
Title: GEMv2: Multilingual NLG Benchmarking in a Single Line of Code Abstract: Evaluation in machine learning is usually informed by past choices, for example which datasets or metrics to use. This standardization enables the comparison on equal footing using leaderboards, but the evaluation choices become sub-optimal ... |
Title: Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Łojasiewicz Functions when the Non-Convexity is Averaged-Out Abstract: Heavy Ball (HB) nowadays is one of the most popular momentum methods in non-convex optimization. It has been widely observed that incorporating the Heavy Ball dynamic... |
Title: Concentration inequalities and optimal number of layers for stochastic deep neural networks Abstract: We state concentration and martingale inequalities for the output of the hidden layers of a stochastic deep neural network (SDNN), as well as for the output of the whole SDNN. These results allow us to introduce... |
Title: FedorAS: Federated Architecture Search under system heterogeneity Abstract: Federated learning (FL) has recently gained considerable attention due to its ability to use decentralised data while preserving privacy. However, it also poses additional challenges related to the heterogeneity of the participating devi... |
Title: Correct and Certify: A New Approach to Self-Supervised 3D-Object Perception Abstract: We consider an object pose estimation and model fitting problem, where - given a partial point cloud of an object - the goal is to estimate the object pose by fitting a CAD model to the sensor data. We solve this problem by com... |
Title: VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives Abstract: Many past works aim to improve visual reasoning in models by supervising feature importance (estimated by model explanation techniques) with human annotations such as highlights of important image regions. However,... |
Title: Constant-Factor Approximation Algorithms for Socially Fair $k$-Clustering Abstract: We study approximation algorithms for the socially fair $(\ell_p, k)$-clustering problem with $m$ groups, whose special cases include the socially fair $k$-median ($p=1$) and socially fair $k$-means ($p=2$) problems. We present (... |
Title: General Univariate Estimation-of-Distribution Algorithms Abstract: We propose a general formulation of a univariate estimation-of-distribution algorithm (EDA). It naturally incorporates the three classic univariate EDAs \emph{compact genetic algorithm}, \emph{univariate marginal distribution algorithm} and \emph... |
Title: Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space Abstract: Sepsis is a leading cause of death in the ICU. It is a disease requiring complex interventions in a short period of time, but its optimal treatment strategy remains uncertain. Evidence suggests tha... |
Title: Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Roles Abstract: Linking neural representations to linguistic factors is crucial in order to build and analyze NLP models interpretable by humans. Among these factors, syntactic roles (e.g. subjects, direct objects,$\dots$) and... |
Title: Active Learning with Safety Constraints Abstract: Active learning methods have shown great promise in reducing the number of samples necessary for learning. As automated learning systems are adopted into real-time, real-world decision-making pipelines, it is increasingly important that such algorithms are design... |
Title: On the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement Abstract: Employing deep neural networks (DNNs) to directly learn filters for multi-channel speech enhancement has potentially two key advantages over a traditional approach combining a linear spat... |
Title: Optimal transport meets noisy label robust loss and MixUp regularization for domain adaptation Abstract: It is common in computer vision to be confronted with domain shift: images which have the same class but different acquisition conditions. In domain adaptation (DA), one wants to classify unlabeled target ima... |
Title: Cold Posteriors through PAC-Bayes Abstract: We investigate the cold posterior effect through the lens of PAC-Bayes generalization bounds. We argue that in the non-asymptotic setting, when the number of training samples is (relatively) small, discussions of the cold posterior effect should take into account that ... |
Title: Neural Inverse Transform Sampler Abstract: Any explicit functional representation $f$ of a density is hampered by two main obstacles when we wish to use it as a generative model: designing $f$ so that sampling is fast, and estimating $Z = \int f$ so that $Z^{-1}f$ integrates to 1. This becomes increasingly compl... |
Title: Ordered Subgraph Aggregation Networks Abstract: Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs. However, there is a limited understanding of how these approaches relate to each other and to the Weisfeiler--L... |
Title: Sharing pattern submodels for prediction with missing values Abstract: Missing values are unavoidable in many applications of machine learning and present a challenge both during training and at test time. When variables are missing in recurring patterns, fitting separate pattern submodels have been proposed as ... |
Title: Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses Abstract: Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental he... |
Title: reStructured Pre-training Abstract: In this work, we try to decipher the internal connection of NLP technology development in the past decades, searching for essence, which rewards us with a (potential) new learning paradigm for NLP tasks, dubbed as reStructured Pre-training (RST). In such a paradigm, the role o... |
Title: Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence' Abstract: We discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based on the Hilbert-Schmidt independence crit... |
Title: Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries Abstract: Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which model graphs as collections of subgraphs. So far, the design space of possible Subgraph GNN architectures as well as their basic theoretical propert... |
Title: Variational Causal Dynamics: Discovering Modular World Models from Interventions Abstract: Latent world models allow agents to reason about complex environments with high-dimensional observations. However, adapting to new environments and effectively leveraging previous knowledge remain significant challenges. W... |
Title: tntorch: Tensor Network Learning with PyTorch Abstract: We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp/Parafac, Tucker, and Tensor Train) under a unified interface. With our library, the user can learn and handle low-rank tensors with automatic differen... |
Title: Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models Abstract: With the rapid development of eXplainable Artificial Intelligence (XAI), a long line of past work has shown concerns about the Out-of-Distribution (OOD) problem in perturbation-based post-hoc XAI models and expl... |
Title: A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta Abstract: Mini-batch SGD with momentum is a fundamental algorithm for learning large predictive models. In this paper we develop a new analytic framework to analyze mini-batch SGD for li... |
Title: Near-optimal control of dynamical systems with neural ordinary differential equations Abstract: Optimal control problems naturally arise in many scientific applications where one wishes to steer a dynamical system from a certain initial state $\mathbf{x}_0$ to a desired target state $\mathbf{x}^*$ in finite time... |
Title: Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior? Abstract: Autonomous vehicles use a variety of sensors and machine-learned models to predict the behavior of surrounding road users. Most of the machine-learned models in the literature focus on quantitative error metric... |
Title: OpenXAI: Towards a Transparent Evaluation of Model Explanations Abstract: While several types of post hoc explanation methods (e.g., feature attribution methods) have been proposed in recent literature, there is little to no work on systematically benchmarking these methods in an efficient and transparent manner... |
Title: Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks Abstract: Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN case, message-passing-based heterogen... |
Title: Noisy $\ell^{0}$-Sparse Subspace Clustering on Dimensionality Reduced Data Abstract: Sparse subspace clustering methods with sparsity induced by $\ell^{0}$-norm, such as $\ell^{0}$-Sparse Subspace Clustering ($\ell^{0}$-SSC)~\citep{YangFJYH16-L0SSC-ijcv}, are demonstrated to be more effective than its $\ell^{1}$... |
Title: AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment Abstract: How to quickly and automatically mine effective information and serve investment decisions has attracted more and more attention from academia and industry. And new challenges have been raised with the global pandem... |
Title: Answer Fast: Accelerating BERT on the Tensor Streaming Processor Abstract: Transformers have become a predominant machine learning workload, they are not only the de-facto standard for natural language processing tasks, but they are also being deployed in other domains such as vision and speech recognition. Many... |
Title: Surgical-VQA: Visual Question Answering in Surgical Scenes using Transformer Abstract: Visual question answering (VQA) in surgery is largely unexplored. Expert surgeons are scarce and are often overloaded with clinical and academic workloads. This overload often limits their time answering questionnaires from pa... |
Title: Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression Abstract: We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge. The multitask aims to recogniz... |
Title: Automated GI tract segmentation using deep learning Abstract: The job of Radiation oncologists is to deliver x-ray beams pointed toward the tumor and at the same time avoid the stomach and intestines. With MR-Linacs (magnetic resonance imaging and linear accelerator systems), oncologists can visualize the positi... |
Title: KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images Abstract: We present KeyCLD, a framework to learn Lagrangian dynamics from images. Learned keypoints represent semantic landmarks in images and can directly represent state dynamics. Interpreting this state as Cartesian coordina... |
Title: ROSE: A RObust and SEcure DNN Watermarking Abstract: Protecting the Intellectual Property rights of DNN models is of primary importance prior to their deployment. So far, the proposed methods either necessitate changes to internal model parameters or the machine learning pipeline, or they fail to meet both the s... |
Title: Agent-based Graph Neural Networks Abstract: We present a novel graph neural network we call AgentNet, which is designed specifically for graph-level tasks. AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size. The architecture of AgentNet differ... |
Title: Auto-Encoding Adversarial Imitation Learning Abstract: Reinforcement learning (RL) provides a powerful framework for decision-making, but its application in practice often requires a carefully designed reward function. Adversarial Imitation Learning (AIL) sheds light on automatic policy acquisition without acces... |
Title: A Systematic Comparison of Phonetic Aware Techniques for Speech Enhancement Abstract: Speech enhancement has seen great improvement in recent years using end-to-end neural networks. However, most models are agnostic to the spoken phonetic content. Recently, several studies suggested phonetic-aware speech enhance... |
Title: Neural Networks as Paths through the Space of Representations Abstract: Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to understand. We develop a simple idea for interpreting the lay... |
Title: Graph Neural Networks as Gradient Flows Abstract: Dynamical systems minimizing an energy are ubiquitous in geometry and physics. We propose a gradient flow framework for GNNs where the equations follow the direction of steepest descent of a learnable energy. This approach allows to explain the GNN evolution from... |
Title: Traffic Congestion Prediction Using Machine Learning Techniques Abstract: The prediction of traffic congestion can serve a crucial role in making future decisions. Although many studies have been conducted regarding congestion, most of these could not cover all the important factors (e.g., weather conditions). W... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.