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Title: Uncertainty-Based Non-Parametric Active Peak Detection Abstract: Active, non-parametric peak detection is considered. As a use case, active source localization is examined and an uncertainty-based sampling scheme algorithm to effectively localize the peak from a few energy measurements is designed. It is shown t... |
Title: Optimising Equal Opportunity Fairness in Model Training Abstract: Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing societal preconceptions. Existing debiasing methods, such as advers... |
Title: Compressive Ptychography using Deep Image and Generative Priors Abstract: Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. It has been extensively used in various areas such as the defense industry or materials science. O... |
Title: Spot-adaptive Knowledge Distillation Abstract: Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks. The typical way of conducting knowledge distillation is to train the student network under the supervision of the teacher network to harness the knowledge at one... |
Title: Dangling-Aware Entity Alignment with Mixed High-Order Proximities Abstract: We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities ... |
Title: Dynamic Bayesian Network Auxiliary ABC-SMC for Hybrid Model Bayesian Inference to Accelerate Biomanufacturing Process Mechanism Learning and Robust Control Abstract: Driven by the critical needs of biomanufacturing 4.0, we present a probabilistic knowledge graph hybrid model characterizing complex spatial-tempor... |
Title: Subverting Fair Image Search with Generative Adversarial Perturbations Abstract: In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the rankin... |
Title: FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs Abstract: Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adap... |
Title: Uncertainty Minimization for Personalized Federated Semi-Supervised Learning Abstract: Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable ... |
Title: DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data Abstract: Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep ... |
Title: Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services Abstract: Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circu... |
Title: A Deep Learning Approach to Dst Index Prediction Abstract: The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm. A negative Dst value means that the Earth's magnetic field is weakene... |
Title: Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning Abstract: Dynamic mechanism design has garnered significant attention from both computer scientists and economists in recent years. By allowing agents to interact with the seller over multiple rounds, where agents' reward f... |
Title: Learning to Solve Vehicle Routing Problems: A Survey Abstract: This paper provides a systematic overview of machine learning methods applied to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been a great interest from both machine learning and operations research communities to solve VRPs eit... |
Title: Assistive Recipe Editing through Critiquing Abstract: There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online recipe data. Prior studies have used pre-trained language models, or relied on... |
Title: COGMEN: COntextualized GNN based Multimodal Emotion recognitioN Abstract: Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions. During a conversation involving various people, a person's emotions are influenced ... |
Title: Soft and Hard Constrained Parametric Generative Schemes for Encoding and Synthesizing Airfoils Abstract: Traditional airfoil parametric technique has significant limitation in modern aerodynamic optimization design.There is a strong demand for developing a parametric method with good intuitiveness, flexibility a... |
Title: KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning Abstract: While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properties of different HPs and measure the transfer ability... |
Title: Optimal Algorithms for Mean Estimation under Local Differential Privacy Abstract: We study the problem of mean estimation of $\ell_2$-bounded vectors under the constraint of local differential privacy. While the literature has a variety of algorithms that achieve the asymptotically optimal rates for this problem... |
Title: Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural Networks Abstract: Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its performance. However, a pre-trained teacher mo... |
Title: dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training Abstract: Distributed training using multiple devices (e.g., GPUs) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed training tends to be far from linea... |
Title: FastRE: Towards Fast Relation Extraction with Convolutional Encoder and Improved Cascade Binary Tagging Framework Abstract: Recent work for extracting relations from texts has achieved excellent performance. However, most existing methods pay less attention to the efficiency, making it still challenging to quick... |
Title: View-labels Are Indispensable: A Multifacet Complementarity Study of Multi-view Clustering Abstract: Consistency and complementarity are two key ingredients for boosting multi-view clustering (MVC). Recently with the introduction of popular contrastive learning, the consistency learning of views has been further... |
Title: Automated Imbalanced Classification via Layered Learning Abstract: In this paper we address imbalanced binary classification (IBC) tasks. Applying resampling strategies to balance the class distribution of training instances is a common approach to tackle these problems. Many state-of-the-art methods find instan... |
Title: LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning Abstract: Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all agents share the same policy or va... |
Title: One Size Does Not Fit All: The Case for Personalised Word Complexity Models Abstract: Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acqu... |
Title: A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning Abstract: Deep reinforcement learning (DRL) has made significant achievements in many real-world applications. But these real-world applications typically can only provide partial observations for making decisions due to occlusions and noisy senso... |
Title: PI-NLF: A Proportional-Integral Approach for Non-negative Latent Factor Analysis Abstract: A high-dimensional and incomplete (HDI) matrix frequently appears in various big-data-related applications, which demonstrates the inherently non-negative interactions among numerous nodes. A non-negative latent factor (NL... |
Title: Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims Abstract: We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pand... |
Title: Holistic Approach to Measure Sample-level Adversarial Vulnerability and its Utility in Building Trustworthy Systems Abstract: Adversarial attack perturbs an image with an imperceptible noise, leading to incorrect model prediction. Recently, a few works showed inherent bias associated with such attack (robustness... |
Title: Contrastive Multi-view Hyperbolic Hierarchical Clustering Abstract: Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raises a less investigated problem, i.e., multi-view hierarchical clus... |
Title: GANimator: Neural Motion Synthesis from a Single Sequence Abstract: We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel an... |
Title: Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks Abstract: Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability ... |
Title: Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization Abstract: Decision making algorithms are used in a multitude of different applications. Conventional approaches for designing decision algorithms employ principled and simplified modelling, based on which one can determine decisions... |
Title: PyDaddy: A Python package for discovering stochastic dynamical equations from timeseries data Abstract: Most real-world ecological dynamics, ranging from ecosystem dynamics to collective animal movement, are inherently stochastic in nature. Stochastic differential equations (SDEs) are a popular modelling framewo... |
Title: Can collaborative learning be private, robust and scalable? Abstract: We investigate the effectiveness of combining differential privacy, model compression and adversarial training to improve the robustness of models against adversarial samples in train- and inference-time attacks. We explore the applications of... |
Title: Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications Abstract: Counting and sampling directed acyclic graphs from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper we show that these tasks can be performed in polynomial time, sol... |
Title: LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural Networks Abstract: Variational quantum algorithms (VQAs) have recently received significant attention from the research community due to their promising performance in Noisy Intermediate-Scale Quantum computers (NISQ). However, VQAs run... |
Title: Unsupervised Mismatch Localization in Cross-Modal Sequential Data Abstract: Content mismatch usually occurs when data from one modality is translated to another, e.g. language learners producing mispronunciations (errors in speech) when reading a sentence (target text) aloud. However, most existing alignment alg... |
Title: What is Right for Me is Not Yet Right for You: A Dataset for Grounding Relative Directions via Multi-Task Learning Abstract: Understanding spatial relations is essential for intelligent agents to act and communicate in the physical world. Relative directions are spatial relations that describe the relative posit... |
Title: Multi-Agent Deep Reinforcement Learning in Vehicular OCC Abstract: Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim ... |
Title: On Disentangled and Locally Fair Representations Abstract: We study the problem of performing classification in a manner that is fair for sensitive groups, such as race and gender. This problem is tackled through the lens of disentangled and locally fair representations. We learn a locally fair representation, s... |
Title: Chemoreception and chemotaxis of a three-sphere swimmer Abstract: The coupled problem of hydrodynamics and solute transport for the Najafi-Golestanian three-sphere swimmer is studied, with the Reynolds number set to zero and P\'eclet numbers (Pe) ranging from 0.06 to 60. The adopted method is the numerical simul... |
Title: KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design Abstract: Knitted fabric exhibits avalanche-like events when deformed: by analogy with eathquakes, we are interested in predicting these "knitquakes". However, as in most analogous seismic mode... |
Title: Mode Reduction for Markov Jump Systems Abstract: Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time. To achieve accurate modeling in practice, one may need a large number of modes, but this may in turn increase the model complexity drastically. Existing... |
Title: Sound Event Classification in an Industrial Environment: Pipe Leakage Detection Use Case Abstract: In this work, a multi-stage Machine Learning (ML) pipeline is proposed for pipe leakage detection in an industrial environment. As opposed to other industrial and urban environments, the environment under study inc... |
Title: Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Abstract: We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernels by interpolating between meta-learning and conventional deep kernel learning. Our approa... |
Title: Communication-Efficient Adaptive Federated Learning Abstract: Federated learning is a machine learning training paradigm that enables clients to jointly train models without sharing their own localized data. However, the implementation of federated learning in practice still faces numerous challenges, such as th... |
Title: Characterizing player's playing styles based on Player Vectors for each playing position in the Chinese Football Super League Abstract: Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of tech... |
Title: CE-based white-box adversarial attacks will not work using super-fitting Abstract: Deep neural networks are widely used in various fields because of their powerful performance. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks, i.e., adding a slight perturbation t... |
Title: Rethinking Classifier and Adversarial Attack Abstract: Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e., not approaching the lower bound of robustness). To... |
Title: A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022 Abstract: A collection of invited non-archival papers for the Conference on Health, Inference, and Learning (CHIL) 2022. This index is incomplete as some authors of invited non-archival presentations opt... |
Title: General sum stochastic games with networked information flows Abstract: Inspired by applications such as supply chain management, epidemics, and social networks, we formulate a stochastic game model that addresses three key features common across these domains: 1) network-structured player interactions, 2) pair-... |
Title: Cognitive Radio Resource Scheduling using Multi agent QLearning for LTE Abstract: In this paper, we propose, implement, and test two novel downlink LTE scheduling algorithms. The implementation and testing of these algorithms were in Matlab, and they are based on the use of Reinforcement Learning, more specifica... |
Title: Spiking Graph Convolutional Networks Abstract: Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be ... |
Title: Finding Bipartite Components in Hypergraphs Abstract: Hypergraphs are important objects to model ternary or higher-order relations of objects, and have a number of applications in analysing many complex datasets occurring in practice. In this work we study a new heat diffusion process in hypergraphs, and employ ... |
Title: Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data Abstract: The analysis of electrocardiogram (ECG) signals can be time consuming as it is performed manually by cardiologists. Therefore, automation through machine learning (ML) classification is being increasingly propos... |
Title: Quantum Extremal Learning Abstract: We propose a quantum algorithm for `extremal learning', which is the process of finding the input to a hidden function that extremizes the function output, without having direct access to the hidden function, given only partial input-output (training) data. The algorithm, call... |
Title: Morphological Wobbling Can Help Robots Learn Abstract: We propose to make the physical characteristics of a robot oscillate while it learns to improve its behavioral performance. We consider quantities such as mass, actuator strength, and size that are usually fixed in a robot, and show that when those quantitie... |
Title: Generative methods for sampling transition paths in molecular dynamics Abstract: Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastability. Simulating transition paths linking one metastab... |
Title: Rapid Locomotion via Reinforcement Learning Abstract: Agile maneuvers such as sprinting and high-speed turning in the wild are challenging for legged robots. We present an end-to-end learned controller that achieves record agility for the MIT Mini Cheetah, sustaining speeds up to 3.9 m/s. This system runs and tu... |
Title: Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations Abstract: We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction and auto-encoding. Our method encodes the v... |
Title: Identifying Cause-and-Effect Relationships of Manufacturing Errors using Sequence-to-Sequence Learning Abstract: In car-body production the pre-formed sheet metal parts of the body are assembled on fully-automated production lines. The body passes through multiple stations in succession, and is processed accordi... |
Title: Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction Abstract: This paper studies the problem of fixing malfunctional 3D objects. While previous works focus on building passive perception models to learn the functionality from static 3D objects, we argue that functionality is r... |
Title: Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics Abstract: Differentiable physics has recently been shown as a powerful tool for solving soft-body manipulation tasks. However, the differentiable physics solver often gets stuck when the initial contact points of the end effectors a... |
Title: Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures Abstract: The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiograph... |
Title: Generative Adversarial Network Based Synthetic Learning and a Novel Domain Relevant Loss Term for Spine Radiographs Abstract: Problem: There is a lack of big data for the training of deep learning models in medicine, characterized by the time cost of data collection and privacy concerns. Generative adversarial n... |
Title: AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching Abstract: This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challen... |
Title: Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands Abstract: Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multi-reference (MR) charact... |
Title: Multi-confound regression adversarial network for deep learning-based diagnosis on highly heterogenous clinical data Abstract: Automated disease detection in medical images using deep learning holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contai... |
Title: Evaluating Context for Deep Object Detectors Abstract: Which object detector is suitable for your context sensitive task? Deep object detectors exploit scene context for recognition differently. In this paper, we group object detectors into 3 categories in terms of context use: no context by cropping the input (... |
Title: New-Onset Diabetes Assessment Using Artificial Intelligence-Enhanced Electrocardiography Abstract: Undiagnosed diabetes is present in 21.4% of adults with diabetes. Diabetes can remain asymptomatic and undetected due to limitations in screening rates. To address this issue, questionnaires, such as the American D... |
Title: Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks Abstract: Physics-informed neural networks (PINNs) leverage neural-networks to find the solutions of partial differential equation (PDE)-constrained optimization problems with initial conditions and boundary co... |
Title: Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes Abstract: This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a tria... |
Title: GreenDB: Toward a Product-by-Product Sustainability Database Abstract: The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommend... |
Title: GANs as Gradient Flows that Converge Abstract: This paper approaches the unsupervised learning problem by gradient descent in the space of probability density functions. Our main result shows that along the gradient flow induced by a distribution-dependent ordinary differential equation (ODE), the unknown data d... |
Title: Understanding Urban Water Consumption using Remotely Sensed Data Abstract: Urban metabolism is an active field of research that deals with the estimation of emissions and resource consumption from urban regions. The analysis could be carried out through a manual surveyor by the implementation of elegant machine ... |
Title: Explainable multi-class anomaly detection on functional data Abstract: In this paper we describe an approach for anomaly detection and its explainability in multivariate functional data. The anomaly detection procedure consists of transforming the series into a vector of features and using an Isolation forest al... |
Title: Detection of Propaganda Techniques in Visuo-Lingual Metaphor in Memes Abstract: The exponential rise of social media networks has allowed the production, distribution, and consumption of data at a phenomenal rate. Moreover, the social media revolution has brought a unique phenomenon to social media platforms cal... |
Title: Immiscible Color Flows in Optimal Transport Networks for Image Classification Abstract: In classification tasks, it is crucial to meaningfully exploit information contained in data. Here, we propose a physics-inspired dynamical system that adapts Optimal Transport principles to effectively leverage color distrib... |
Title: A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior Abstract: Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach re... |
Title: Over-The-Air Federated Learning under Byzantine Attacks Abstract: Federated learning (FL) is a promising solution to enable many AI applications, where sensitive datasets from distributed clients are needed for collaboratively training a global model. FL allows the clients to participate in the training phase, g... |
Title: Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing Abstract: We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary research in a... |
Title: Low Dimensional Invariant Embeddings for Universal Geometric Learning Abstract: This paper studies separating invariants: mappings on $d$-dimensional semi-algebraic subsets of $D$ dimensional Euclidean domains which are invariant to semi-algebraic group actions and separate orbits. The motivation for this study ... |
Title: Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations Abstract: We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to model the behavior of teams performing sequential tasks in Markovian domains. In contrast to existing multi-agent imitation learning t... |
Title: Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery Abstract: Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerat... |
Title: Large Scale Transfer Learning for Differentially Private Image Classification Abstract: Differential Privacy (DP) provides a formal framework for training machine learning models with individual example level privacy. In the field of deep learning, Differentially Private Stochastic Gradient Descent (DP-SGD) has ... |
Title: Variance Reduction based Partial Trajectory Reuse to Accelerate Policy Gradient Optimization Abstract: We extend the idea underlying the success of green simulation assisted policy gradient (GS-PG) to partial historical trajectory reuse for infinite-horizon Markov Decision Processes (MDP). The existing GS-PG met... |
Title: IMU Based Deep Stride Length Estimation With Self-Supervised Learning Abstract: Stride length estimation using inertial measurement unit (IMU) sensors is getting popular recently as one representative gait parameter for health care and sports training. The traditional estimation method requires some explicit cal... |
Title: Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports Abstract: Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP. However, training or fine-tuning these models for individual tasks can be time consum... |
Title: Optimally tackling covariate shift in RKHS-based nonparametric regression Abstract: We study the covariate shift problem in the context of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We focus on two natural families of covariate shift problems defined using the likelihood ratios betw... |
Title: Learning Optimal Propagation for Graph Neural Networks Abstract: Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph might not be optimal in terms of specific downstream tasks, because ... |
Title: Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design Abstract: Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While e... |
Title: Differentially Private Generalized Linear Models Revisited Abstract: We study the problem of $(\epsilon,\delta)$-differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is when the loss is smooth and non-negative but not nec... |
Title: Generative Adversarial Neural Operators Abstract: We propose the generative adversarial neural operator (GANO), a generative model paradigm for learning probabilities on infinite-dimensional function spaces. The natural sciences and engineering are known to have many types of data that are sampled from infinite-... |
Title: A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition Abstract: Despite the success of deep learning in speech recognition, multi-dialect speech recognition remains a difficult problem. Although dialect-specific acoustic models are known to perform well in general, they are not easy to m... |
Title: Quantification of Robotic Surgeries with Vision-Based Deep Learning Abstract: Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term pat... |
Title: Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation Abstract: Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique inst... |
Title: Incremental Data-Uploading for Full-Quantum Classification Abstract: The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy intermediate scale quantum (NISQ) devices. Encoding high dimensio... |
Title: Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization Abstract: Nonconvex regularization has been popularly used in low-rank matrix learning. However, extending it for low-rank tensor learning is still computationally expensive. To address this problem, we develop an efficient solver for... |
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