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Title: Generalization bounds for learning under graph-dependence: A survey Abstract: Traditional statistical learning theory relies on the assumption that data are identically and independently generated from a given distribution (i.i.d.). The independently distributed assumption, on the other hand, fails to hold in ma... |
Title: Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification Abstract: Gene annotation addresses the problem of predicting unknown associations between gene and functions (e.g., biological processes) of a specific organism. Despite recent advances, the c... |
Title: Preprocessing Reward Functions for Interpretability Abstract: In many real-world applications, the reward function is too complex to be manually specified. In such cases, reward functions must instead be learned from human feedback. Since the learned reward may fail to represent user preferences, it is important... |
Title: Deformable Butterfly: A Highly Structured and Sparse Linear Transform Abstract: We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimensions. It inherits the fine-to-coarse-grained learnab... |
Title: Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset Abstract: One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is... |
Title: An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development Abstract: Recent years have witnessed an exponential growth in developing deep learning (DL) models for the time-series electricity forecasting in power systems. However, most of the proposed models are d... |
Title: $p$-Generalized Probit Regression and Scalable Maximum Likelihood Estimation via Sketching and Coresets Abstract: We study the $p$-generalized probit regression model, which is a generalized linear model for binary responses. It extends the standard probit model by replacing its link function, the standard norma... |
Title: Improving Question Answering over Knowledge Graphs Using Graph Summarization Abstract: Question Answering (QA) systems over Knowledge Graphs (KGs) (KGQA) automatically answer natural language questions using triples contained in a KG. The key idea is to represent questions and entities of a KG as low-dimensional... |
Title: Unsupervised Learning of Temporal Abstractions with Slot-based Transformers Abstract: The discovery of reusable sub-routines simplifies decision-making and planning in complex reinforcement learning problems. Previous approaches propose to learn such temporal abstractions in a purely unsupervised fashion through... |
Title: Impact of Dataset on Acoustic Models for Automatic Speech Recognition Abstract: In Automatic Speech Recognition, GMM-HMM had been widely used for acoustic modelling. With the current advancement of deep learning, the Gaussian Mixture Model (GMM) from acoustic models has been replaced with Deep Neural Network, na... |
Title: Fast and computationally efficient generative adversarial network algorithm for unmanned aerial vehicle-based network coverage optimization Abstract: The challenge of dynamic traffic demand in mobile networks is tackled by moving cells based on unmanned aerial vehicles. Considering the tremendous potential of un... |
Title: Repairing Group-Level Errors for DNNs Using Weighted Regularization Abstract: Deep Neural Networks (DNNs) have been widely used in software making decisions impacting people's lives. However, they have been found to exhibit severe erroneous behaviors that may lead to unfortunate outcomes. Previous work shows tha... |
Title: Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning Abstract: Graph convolution networks (GCNs) are currently mainstream in learning with irregular data. These models rely on message passing and attention mechanisms that capture context and node-to-node relationships. With mu... |
Title: EmotionNAS: Two-stream Architecture Search for Speech Emotion Recognition Abstract: Speech emotion recognition (SER) is a crucial research topic in human-computer interactions. Existing works are mainly based on manually designed models. Despite their great success, these methods heavily rely on historical exper... |
Title: StretchBEV: Stretching Future Instance Prediction Spatially and Temporally Abstract: In self-driving, predicting future in terms of location and motion of all the agents around the vehicle is a crucial requirement for planning. Recently, a new joint formulation of perception and prediction has emerged by fusing ... |
Title: Understanding the Difficulty of Training Physics-Informed Neural Networks on Dynamical Systems Abstract: Physics-informed neural networks (PINNs) seamlessly integrate data and physical constraints into the solving of problems governed by differential equations. In settings with little labeled training data, thei... |
Title: HYDRA: Competing convolutional kernels for fast and accurate time series classification Abstract: We demonstrate a simple connection between dictionary methods for time series classification, which involve extracting and counting symbolic patterns in time series, and methods based on transforming input time seri... |
Title: Gransformer: Transformer-based Graph Generation Abstract: Transformers have become widely used in modern models for various tasks such as natural language processing and machine vision. This paper proposes Gransformer, an algorithm for generating graphs based on the Transformer. We extend a simple autoregressive... |
Title: Common Failure Modes of Subcluster-based Sampling in Dirichlet Process Gaussian Mixture Models -- and a Deep-learning Solution Abstract: The Dirichlet Process Gaussian Mixture Model (DPGMM) is often used to cluster data when the number of clusters is unknown. One main DPGMM inference paradigm relies on sampling.... |
Title: FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning Abstract: Multi-task learning (MTL) is a novel framework to learn several tasks simultaneously with a single shared network where each task has its distinct personalized header network for fine-tuning. MTL can be implemented in federated... |
Title: ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation Abstract: Chest Computational Tomography (CT) scans present low cost, speed and objectivity for COVID-19 diagnosis and deep learning methods have shown great promise in assisting the analysis and interpretation of these images. M... |
Title: Learning to Mediate Disparities Towards Pragmatic Communication Abstract: Human communication is a collaborative process. Speakers, on top of conveying their own intent, adjust the content and language expressions by taking the listeners into account, including their knowledge background, personalities, and phys... |
Title: Image Compression and Actionable Intelligence With Deep Neural Networks Abstract: If a unit cannot receive intelligence from a source due to external factors, we consider them disadvantaged users. We categorize this as a preoccupied unit working on a low connectivity device on the edge. This case requires that w... |
Title: Chain-based Discriminative Autoencoders for Speech Recognition Abstract: In our previous work, we proposed a discriminative autoencoder (DcAE) for speech recognition. DcAE combines two training schemes into one. First, since DcAE aims to learn encoder-decoder mappings, the squared error between the reconstructed... |
Title: Speech-enhanced and Noise-aware Networks for Robust Speech Recognition Abstract: Compensation for channel mismatch and noise interference is essential for robust automatic speech recognition. Enhanced speech has been introduced into the multi-condition training of acoustic models to improve their generalization ... |
Title: LAMBDA: Covering the Solution Set of Black-Box Inequality by Search Space Quantization Abstract: Black-box functions are broadly used to model complex problems that provide no explicit information but the input and output. Despite existing studies of black-box function optimization, the solution set satisfying a... |
Title: Searching for Network Width with Bilaterally Coupled Network Abstract: Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constraints. To fulfill the searching, a one-shot supernet is usually ... |
Title: Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning Abstract: Assembly of multi-part physical structures is both a valuable end product for autonomous robotics, as well as a valuable diagnostic task for open-ended training of embodied intelligent agents. We introduce a natura... |
Title: High Dimensional Quantum Machine Learning With Small Quantum Computers Abstract: Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation techniques can be applied for evaluating a quantum c... |
Title: Efficient-VDVAE: Less is more Abstract: Hierarchical VAEs have emerged in recent years as a reliable option for maximum likelihood estimation. However, instability issues and demanding computational requirements have hindered research progress in the area. We present simple modifications to the Very Deep VAE to ... |
Title: Fast fluorescence lifetime imaging analysis via extreme learning machine Abstract: We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM) using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared t... |
Title: JAX-FLUIDS: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows Abstract: Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes equations describe fluid flows and are representative of nonlinear physical systems with complex s... |
Title: Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion Abstract: Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory prediction system to model the multi-modality of future motion states. Unlike existing stochastic trajectory prediction methods which usually use ... |
Title: L3Cube-MahaHate: A Tweet-based Marathi Hate Speech Detection Dataset and BERT models Abstract: Social media platforms are used by a large number of people prominently to express their thoughts and opinions. However, these platforms have contributed to a substantial amount of hateful and abusive content as well. ... |
Title: Origins of Low-dimensional Adversarial Perturbations Abstract: In this note, we initiate a rigorous study of the phenomenon of low-dimensional adversarial perturbations in classification. These are adversarial perturbations wherein, unlike the classical setting, the attacker's search is limited to a low-dimensio... |
Title: Ensemble Spectral Prediction (ESP) Model for Metabolite Annotation Abstract: A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches ha... |
Title: A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization Abstract: We investigate nonlinear prediction in an online setting and introduce a hybrid model that effectively mitigates, via an end-to-end architecture, the need for hand-designed features and manual model selection issues of conve... |
Title: FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations Abstract: In this paper we introduce "Federated Learning Utilities and Tools for Experimentation" (FLUTE), a high-performance open source platform for federated learning research and offline simulations. The goal of FLUTE... |
Title: A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration Abstract: Deep Neural Networks ( DNN s) are known to make overconfident mistakes, which makes their use problematic in safety-critical applications. State-of-the-art ( SOTA ) calibration techniques improve on the ... |
Title: Cluster Algebras: Network Science and Machine Learning Abstract: Cluster algebras have recently become an important player in mathematics and physics. In this work, we investigate them through the lens of modern data science, specifically with techniques from network science and machine-learning. Network analysi... |
Title: Optimal quantum kernels for small data classification Abstract: While quantum machine learning (ML) has been proposed to be one of the most promising applications of quantum computing, how to build quantum ML models that outperform classical ML remains a major open question. Here, we demonstrate an algorithm for... |
Title: Quasi-Newton Iteration in Deterministic Policy Gradient Abstract: This paper presents a model-free approximation for the Hessian of the performance of deterministic policies to use in the context of Reinforcement Learning based on Quasi-Newton steps in the policy parameters. We show that the approximate Hessian ... |
Title: Which Generative Adversarial Network Yields High-Quality Synthetic Medical Images: Investigation Using AMD Image Datasets Abstract: Deep learning has been proposed for the assessment and classification of medical images. However, many medical image databases with appropriately labeled and annotated images are sm... |
Title: Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis Abstract: The need for a large amount of labeled data in the supervised setting has led recent studies to utilize self-supervised learning to pre-train deep neural networks using unlabeled data. Many self-supervised training stra... |
Title: Data Selection Curriculum for Neural Machine Translation Abstract: Neural Machine Translation (NMT) models are typically trained on heterogeneous data that are concatenated and randomly shuffled. However, not all of the training data are equally useful to the model. Curriculum training aims to present the data t... |
Title: Self-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images Abstract: In the field of soft materials, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine l... |
Title: Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities Abstract: As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also changing accordingly. Taking advantage of the fact that visual modalities such... |
Title: A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies Abstract: Sepsis is a leading cause of mortality and its treatment is very expensive. Sepsis treatment is also very challenging because there is no consensus on what interventions work best and different patients re... |
Title: Predicting Peak Day and Peak Hour of Electricity Demand with Ensemble Machine Learning Abstract: Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and... |
Title: Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals Abstract: We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals. We show that the multiply robust formula for the dynamic treatment regime ... |
Title: Improving robustness of jet tagging algorithms with adversarial training Abstract: Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tag... |
Title: Using Multiple Instance Learning for Explainable Solar Flare Prediction Abstract: In this work we leverage a weakly-labeled dataset of spectral data from NASAs IRIS satellite for the prediction of solar flares using the Multiple Instance Learning (MIL) paradigm. While standard supervised learning models expect a... |
Title: On efficient algorithms for computing near-best polynomial approximations to high-dimensional, Hilbert-valued functions from limited samples Abstract: Sparse polynomial approximation has become indispensable for approximating smooth, high- or infinite-dimensional functions from limited samples. This is a key tas... |
Title: Concept Embedding Analysis: A Review Abstract: Deep neural networks (DNNs) have found their way into many applications with potential impact on the safety, security, and fairness of human-machine-systems. Such require basic understanding and sufficient trust by the users. This motivated the research field of exp... |
Title: Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA Abstract: Locally Linear Embedding (LLE) is a nonlinear spectral dimensionality reduction and manifold learning method. It has two main steps which are linear reconstruction and linear embedding of points in the input... |
Title: SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks Abstract: While (message-passing) graph neural networks have clear limitations in approximating permutation-equivariant functions over graphs or general relational data, more expressive, higher-order graph neural networks do not scale to large graph... |
Title: Canary Extraction in Natural Language Understanding Models Abstract: Natural Language Understanding (NLU) models can be trained on sensitive information such as phone numbers, zip-codes etc. Recent literature has focused on Model Inversion Attacks (ModIvA) that can extract training data from model parameters. In... |
Title: Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gaps Abstract: We consider a challenging theoretical problem in offline reinforcement learning (RL): obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under only realizability-type assumption... |
Title: Neural Network Layers for Prediction of Positive Definite Elastic Stiffness Tensors Abstract: Machine learning models can be used to predict physical quantities like homogenized elasticity stiffness tensors, which must always be symmetric positive definite (SPD) based on conservation arguments. Two datasets of h... |
Title: SolidGen: An Autoregressive Model for Direct B-rep Synthesis Abstract: The Boundary representation (B-rep) format is the de-facto shape representation in computer-aided design (CAD) to model watertight solid objects. Recent approaches to generating CAD models have focused on learning sketch-and-extrude modeling ... |
Title: Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point Abstract: We propose cooperative edge-assisted dynamic federated learning (CE-FL). CE-FL introduces a distributed machine learning (ML) architecture, where data collection is carried out at the end devices, while t... |
Title: Mode decomposition-based time-varying phase synchronization for fMRI Data Abstract: Recently there has been significant interest in measuring time-varying functional connectivity (TVC) between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the ... |
Title: Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization Abstract: Tuning machine parameters of particle accelerators is a repetitive and time-consuming task, that is challenging to automate. While many off-the-shelf optimization algorithms are available, in practice their use is limited ... |
Title: Current Source Localization Using Deep Prior with Depth Weighting Abstract: This paper proposes a novel neuronal current source localization method based on Deep Prior that represents a more complicated prior distribution of current source using convolutional networks. Deep Prior has been suggested as a means of... |
Title: Transfer of codebook latent factors for cross-domain recommendation with non-overlapping data Abstract: Recommender systems based on collaborative filtering play a vital role in many E-commerce applications as they guide the user in finding their items of interest based on the user's past transactions and feedba... |
Title: EYNet: Extended YOLO for Airport Detection in Remote Sensing Images Abstract: Nowadays, airport detection in remote sensing images has attracted considerable attention due to its strategic role in civilian and military scopes. In particular, uncrewed and operated aerial vehicles must immediately detect safe area... |
Title: Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey Abstract: Deep neuroevolution and deep Reinforcement Learning have received a lot of attention in the last years. Some works have compared them, highlighting theirs pros and cons, but an emerging trend consists in combining them so a... |
Title: Quantum continual learning of quantum data realizing knowledge backward transfer Abstract: For the goal of strong artificial intelligence that can mimic human-level intelligence, AI systems would have the ability to adapt to ever-changing scenarios and learn new knowledge continuously without forgetting previous... |
Title: Data Augmentation Strategies for Improving Sequential Recommender Systems Abstract: Sequential recommender systems have recently achieved significant performance improvements with the exploitation of deep learning (DL) based methods. However, although various DL-based methods have been introduced, most of them o... |
Title: Contrastive Graph Learning for Population-based fMRI Classification Abstract: Contrastive self-supervised learning has recently benefited fMRI classification with inductive biases. Its weak label reliance prevents overfitting on small medical datasets and tackles the high intraclass variances. Nonetheless, exist... |
Title: A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies Abstract: In the last a few decades, deep neural networks have achieved remarkable success in machine learning, computer vision, and pattern recognition. Recent studies however show that neural networks (both s... |
Title: Joint Transformer/RNN Architecture for Gesture Typing in Indic Languages Abstract: Gesture typing is a method of typing words on a touch-based keyboard by creating a continuous trace passing through the relevant keys. This work is aimed at developing a keyboard that supports gesture typing in Indic languages. We... |
Title: Computationally efficient joint coordination of multiple electric vehicle charging points using reinforcement learning Abstract: A major challenge in todays power grid is to manage the increasing load from electric vehicle (EV) charging. Demand response (DR) solutions aim to exploit flexibility therein, i.e., th... |
Title: Metropolis-Hastings Data Augmentation for Graph Neural Networks Abstract: Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a prevalent remedy to improve the generalization ability of ... |
Title: Distributed data analytics Abstract: Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are a key component of online service providers. The financial industry has adopted ML to harness large volumes of data in areas such as fraud detection, r... |
Title: MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain Abstract: This work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites. Our multimodal model i... |
Title: SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks Abstract: Federated learning (FL) is a key enabler for efficient communication and computing leveraging devices' distributed computing capabilities. However, applying FL in practice is challenging due to the local devices' hetero... |
Title: A Roadmap for Big Model Abstract: With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present, there is a lack of research w... |
Title: Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos Abstract: Action recognition models have shown a promising capability to classify human actions in short video clips. In a real scenario, multiple correlated human actions commonly occur in particular orders, forming semantically meaning... |
Title: Robust No-Regret Learning in Min-Max Stackelberg Games Abstract: The behavior of no-regret learning algorithms is well understood in two-player min-max (i.e, zero-sum) games. In this paper, we investigate the behavior of no-regret learning in min-max games with dependent strategy sets, where the strategy of the ... |
Title: Automated Thermal Screening for COVID-19 using Machine Learning Abstract: In the last two years, millions of lives have been lost due to COVID-19. Despite the vaccination programmes for a year, hospitalization rates and deaths are still high due to the new variants of COVID-19. Stringent guidelines and COVID-19 ... |
Title: Nash, Conley, and Computation: Impossibility and Incompleteness in Game Dynamics Abstract: Under what conditions do the behaviors of players, who play a game repeatedly, converge to a Nash equilibrium? If one assumes that the players' behavior is a discrete-time or continuous-time rule whereby the current mixed ... |
Title: A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection Abstract: Fake news on social media is increasingly regarded as one of the most concerning issues. Low cost, simple accessibility via social platforms, and a plethora of low-budget online news sou... |
Title: Discovering dynamical features of Hodgkin-Huxley-type model of physiological neuron using artificial neural network Abstract: We consider Hodgkin-Huxley-type model that is a stiff ODE system with two fast and one slow variables. For the parameter ranges under consideration the original version of the model has u... |
Title: Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding Abstract: The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output ... |
Title: Reverse Engineering of Imperceptible Adversarial Image Perturbations Abstract: It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary. There has been a vast volume of research to generate and defend such adversarial atta... |
Title: NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows Abstract: Deep Learning (DL) algorithms are becoming increasingly popular for the reconstruction of high-resolution turbulent flows (aka super-resolution). However, current DL approaches perform spatially uniform super-resolution - a key pe... |
Title: How Do We Fail? Stress Testing Perception in Autonomous Vehicles Abstract: Autonomous vehicles (AVs) rely on environment perception and behavior prediction to reason about agents in their surroundings. These perception systems must be robust to adverse weather such as rain, fog, and snow. However, validation of ... |
Title: A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management Abstract: The process of continuously reallocating funds into financial assets, aiming to increase the expected return of investment and minimizing the risk, is known as portfolio management. Processing sp... |
Title: A Robust Optimization Method for Label Noisy Datasets Based on Adaptive Threshold: Adaptive-k Abstract: SGD does not produce robust results on datasets with label noise. Because the gradients calculated according to the losses of the noisy samples cause the optimization process to go in the wrong direction. In t... |
Title: AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning Abstract: Automatic Time Series Forecasting (TSF) model design which aims to help users to efficiently design suitable forecasting model for the given time series data scenarios, is a novel research topic to be urgently solved. In ... |
Title: Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices Abstract: The area under the ROC curve (AUROC) has been vigorously applied for imbalanced classification and moreover combined with deep learning techniques. However, there is no existing work that provides sound information for peers t... |
Title: mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations Abstract: The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomput... |
Title: Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems Abstract: Modal split prediction in transportation networks has the potential to support network operators in managing traffic congestion and improving transit service reliability. We focus on the problem of hourly predict... |
Title: How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective Abstract: The lack of adversarial robustness has been recognized as an important issue for state-of-the-art machine learning (ML) models, e.g., deep neural networks (DNNs). Thereby, robustifying ML models against adversarial attacks is... |
Title: Denoising Likelihood Score Matching for Conditional Score-based Data Generation Abstract: Many existing conditional score-based data generation methods utilize Bayes' theorem to decompose the gradients of a log posterior density into a mixture of scores. These methods facilitate the training procedure of conditi... |
Title: OneLabeler: A Flexible System for Building Data Labeling Tools Abstract: Labeled datasets are essential for supervised machine learning. Various data labeling tools have been built to collect labels in different usage scenarios. However, developing labeling tools is time-consuming, costly, and expertise-demandin... |
Title: Causality Inspired Representation Learning for Domain Generalization Abstract: Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen target domain. The mainstream is to leverage statistical models to model th... |
Title: A General Survey on Attention Mechanisms in Deep Learning Abstract: Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. The v... |
Title: A Unified Study of Machine Learning Explanation Evaluation Metrics Abstract: The growing need for trustworthy machine learning has led to the blossom of interpretability research. Numerous explanation methods have been developed to serve this purpose. However, these methods are deficiently and inappropriately ev... |
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