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Title: Wound Severity Classification using Deep Neural Network Abstract: The classification of wound severity is a critical step in wound diagnosis. An effective classifier can help wound professionals categorize wound conditions more quickly and affordably, allowing them to choose the best treatment option. This study... |
Title: An Extendable, Efficient and Effective Transformer-based Object Detector Abstract: Transformers have been widely used in numerous vision problems especially for visual recognition and detection. Detection transformers are the first fully end-to-end learning systems for object detection, while vision transformers... |
Title: Fair Classification under Covariate Shift and Missing Protected Attribute -- an Investigation using Related Features Abstract: This study investigated the problem of fair classification under Covariate Shift and missing protected attribute using a simple approach based on the use of importance-weights to handle ... |
Title: LRH-Net: A Multi-Level Knowledge Distillation Approach for Low-Resource Heart Network Abstract: An electrocardiogram (ECG) monitors the electrical activity generated by the heart and is used to detect fatal cardiovascular diseases (CVDs). Conventionally, to capture the precise electrical activity, clinical exper... |
Title: Limit theorems of Chatterjee's rank correlation Abstract: Establishing the limiting distribution of Chatterjee's rank correlation for a general, possibly non-independent, pair of random variables has been eagerly awaited to many. This paper shows that (a) Chatterjee's rank correlation is asymptotically normal as... |
Title: Federated Learning Cost Disparity for IoT Devices Abstract: Federated learning (FL) promotes predictive model training at the Internet of things (IoT) devices by evading data collection cost in terms of energy, time, and privacy. We model the learning gain achieved by an IoT device against its participation cost... |
Title: NICO++: Towards Better Benchmarking for Domain Generalization Abstract: Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for domain generalization (... |
Title: Self-Aware Personalized Federated Learning Abstract: In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives may not be exactly aligned. Inspired by Bayesian hierarchical models, we develo... |
Title: A Novel ASIC Design Flow using Weight-Tunable Binary Neurons as Standard Cells Abstract: In this paper, we describe a design of a mixed signal circuit for a binary neuron (a.k.a perceptron, threshold logic gate) and a methodology for automatically embedding such cells in ASICs. The binary neuron, referred to as ... |
Title: Learning Compositional Representations for Effective Low-Shot Generalization Abstract: We propose Recognition as Part Composition (RPC), an image encoding approach inspired by human cognition. It is based on the cognitive theory that humans recognize complex objects by components, and that they build a small com... |
Title: A Data-Driven Methodology for Considering Feasibility and Pairwise Likelihood in Deep Learning Based Guitar Tablature Transcription Systems Abstract: Guitar tablature transcription is an important but understudied problem within the field of music information retrieval. Traditional signal processing approaches o... |
Title: Exploiting Embodied Simulation to Detect Novel Object Classes Through Interaction Abstract: In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction. We train a reinforcement learning policy on a stacking task given a known object type, and then observe th... |
Title: Non-Parallel Text Style Transfer with Self-Parallel Supervision Abstract: The performance of existing text style transfer models is severely limited by the non-parallel datasets on which the models are trained. In non-parallel datasets, no direct mapping exists between sentences of the source and target style; t... |
Title: FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergence Abstract: As a distributed learning paradigm, Federated Learning (FL) faces the communication bottleneck issue due to many rounds of model synchronization and aggregation. Heterogeneous data further deteriorates th... |
Title: A Practical Cross-Device Federated Learning Framework over 5G Networks Abstract: The concept of federated learning (FL) was first proposed by Google in 2016. Thereafter, FL has been widely studied for the feasibility of application in various fields due to its potential to make full use of data without compromis... |
Title: Trinary Tools for Continuously Valued Binary Classifiers Abstract: Classification methods for binary (yes/no) tasks often produce a continuously valued score. Machine learning practitioners must perform model selection, calibration, discretization, performance assessment, tuning, and fairness assessment. Such ta... |
Title: Characterizing and Understanding Distributed GNN Training on GPUs Abstract: Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a widely adopted approach is distributed training which accele... |
Title: A dynamical systems based framework for dimension reduction Abstract: We propose a novel framework for learning a low-dimensional representation of data based on nonlinear dynamical systems, which we call dynamical dimension reduction (DDR). In the DDR model, each point is evolved via a nonlinear flow towards a ... |
Title: Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors Abstract: Given a multivariate big time series, can we detect anomalies as soon as they occur? Many existing works detect anomalies by learning how much a time series deviates away from what it should be in the reconstruction framework. Howe... |
Title: On Arbitrary Compression for Decentralized Consensus and Stochastic Optimization over Directed Networks Abstract: We study the decentralized consensus and stochastic optimization problems with compressed communications over static directed graphs. We propose an iterative gradient-based algorithm that compresses ... |
Title: TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos Abstract: The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challen... |
Title: TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval Abstract: Entity retrieval--retrieving information about entity mentions in a query--is a key step in open-domain tasks, such as question answering or fact checking. However, state-of-the-art entity retrievers struggle to retrieve rare entities for am... |
Title: Usage of specific attention improves change point detection Abstract: The change point is a moment of an abrupt alteration in the data distribution. Current methods for change point detection are based on recurrent neural methods suitable for sequential data. However, recent works show that transformers based on... |
Title: Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond Abstract: The vast majority of existing algorithms for unsupervised domain adaptation (UDA) focus on adapting from a labeled source domain to an unlabeled target domain directly in a one-off way. Gradual domain adaptation (GDA), ... |
Title: A Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates Abstract: In this study, we examine a clustering problem in which the covariates of each individual element in a dataset are associated with an uncertainty specific to that element. More specifically, we consider a clusteri... |
Title: TigerLily: Finding drug interactions in silico with the Graph Abstract: Tigerlily is a TigerGraph based system designed to solve the drug interaction prediction task. In this machine learning task, we want to predict whether two drugs have an adverse interaction. Our framework allows us to solve this highly rele... |
Title: How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct Abstract: In this paper, we introduce $\mathsf{CO}_3$, an algorithm for communication-efficiency federated Deep Neural Network (DNN) training.$\mathsf{CO}_3$ takes its name from three processing applied steps which reduce the communic... |
Title: Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey Abstract: Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature representations by a representation learning... |
Title: Fast optimization of common basis for matrix set through Common Singular Value Decomposition Abstract: SVD (singular value decomposition) is one of the basic tools of machine learning, allowing to optimize basis for a given matrix. However, sometimes we have a set of matrices $\{A_k\}_k$ instead, and would like ... |
Title: Joint Multi-view Unsupervised Feature Selection and Graph Learning Abstract: Despite the recent progress, the existing multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize either cluster structure or similarity structure to guide the feature selectio... |
Title: Visio-Linguistic Brain Encoding Abstract: Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding aims at constructing fMRI brain activity given a stimulus. There exists a plethora of neural ... |
Title: Differentiable Time-Frequency Scattering in Kymatio Abstract: Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales. It offers an idealized model of spectrotemporal receptive fields (STRF) in the prim... |
Title: Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime Abstract: We propose a simple modification to the iterative hard thresholding (IHT) algorithm, which recovers asymptotically sparser solutions as a function of the condition number. When aiming to minimize a c... |
Title: Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments Abstract: This paper studies the problem of expected loss minimization given a data distribution that is dependent on the decision-maker's action and evolves dynamically in time according to a geometric decay process. Novel algor... |
Title: A Convergence Analysis of Nesterov's Accelerated Gradient Method in Training Deep Linear Neural Networks Abstract: Momentum methods, including heavy-ball~(HB) and Nesterov's accelerated gradient~(NAG), are widely used in training neural networks for their fast convergence. However, there is a lack of theoretical... |
Title: Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology Abstract: Background: Breast cancer has the highest prevalence in women globally. The classification and diagnosis of breast cancer and its histopathological images have always been a hot spot of clinic... |
Title: An alternative approach for distributed parameter estimation under Gaussian settings Abstract: This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussian distribution. The sensor measurement... |
Title: Backward Reachability Analysis for Neural Feedback Loops Abstract: The increasing prevalence of neural networks (NNs) in safety-critical applications calls for methods to certify their behavior and guarantee safety. This paper presents a backward reachability approach for safety verification of neural feedback l... |
Title: A high-resolution canopy height model of the Earth Abstract: The worldwide variation in vegetation height is fundamental to the global carbon cycle and central to the functioning of ecosystems and their biodiversity. Geospatially explicit and, ideally, highly resolved information is required to manage terrestria... |
Title: Time Series Clustering for Grouping Products Based on Price and Sales Patterns Abstract: Developing technology and changing lifestyles have made online grocery delivery applications an indispensable part of urban life. Since the beginning of the COVID-19 pandemic, the demand for such applications has dramaticall... |
Title: Active Learning with Weak Labels for Gaussian Processes Abstract: Annotating data for supervised learning can be costly. When the annotation budget is limited, active learning can be used to select and annotate those observations that are likely to give the most gain in model performance. We propose an active le... |
Title: Extracting Targeted Training Data from ASR Models, and How to Mitigate It Abstract: Recent work has designed methods to demonstrate that model updates in ASR training can leak potentially sensitive attributes of the utterances used in computing the updates. In this work, we design the first method to demonstrate... |
Title: AutoMLBench: A Comprehensive Experimental Evaluation of Automated Machine Learning Frameworks Abstract: Nowadays, machine learning is playing a crucial role in harnessing the power of the massive amounts of data that we are currently producing every day in our digital world. With the booming demand for machine l... |
Title: Strengthening Subcommunities: Towards Sustainable Growth in AI Research Abstract: AI's rapid growth has been felt acutely by scholarly venues, leading to growing pains within the peer review process. These challenges largely center on the inability of specific subareas to identify and evaluate work that is appro... |
Title: Subspace Nonnegative Matrix Factorization for Feature Representation Abstract: Traditional nonnegative matrix factorization (NMF) learns a new feature representation on the whole data space, which means treating all features equally. However, a subspace is often sufficient for accurate representation in practica... |
Title: StableMoE: Stable Routing Strategy for Mixture of Experts Abstract: The Mixture-of-Experts (MoE) technique can scale up the model size of Transformers with an affordable computational overhead. We point out that existing learning-to-route MoE methods suffer from the routing fluctuation issue, i.e., the target ex... |
Title: L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models Abstract: Code-switching occurs when more than one language is mixed in a given sentence or a conversation. This phenomenon is more prominent on social media platforms and its adoption is increasing over time. Therefore c... |
Title: Dynamic Network Adaptation at Inference Abstract: Machine learning (ML) inference is a real-time workload that must comply with strict Service Level Objectives (SLOs), including latency and accuracy targets. Unfortunately, ensuring that SLOs are not violated in inference-serving systems is challenging due to inh... |
Title: Robust, Nonparametric, Efficient Decomposition of Spectral Peaks under Distortion and Interference Abstract: We propose a decomposition method for the spectral peaks in an observed frequency spectrum, which is efficiently acquired by utilizing the Fast Fourier Transform. In contrast to the traditional methods of... |
Title: STONet: A Neural-Operator-Driven Spatio-temporal Network Abstract: Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks. However, these models are us... |
Title: CHAI: A CHatbot AI for Task-Oriented Dialogue with Offline Reinforcement Learning Abstract: Conventionally, generation of natural language for dialogue agents may be viewed as a statistical learning problem: determine the patterns in human-provided data and generate appropriate responses with similar statistical... |
Title: Deep Equilibrium Optical Flow Estimation Abstract: Many recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms by encouraging iterative refinements toward a stable flow estimation. However, these RNNs impose large computation and memory ov... |
Title: Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images Abstract: Remote-sensing (RS) Change Detection (CD) aims to detect "changes of interest" from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attributed to the large amo... |
Title: SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation Abstract: A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine... |
Title: Comparative analysis of machine learning and numerical modeling for combined heat transfer in Polymethylmethacrylate Abstract: This study compares different methods to predict the simultaneous effects of conductive and radiative heat transfer in a Polymethylmethacrylate (PMMA) sample. PMMA is a kind of polymer u... |
Title: 3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition Abstract: 3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. In the chapter we are intere... |
Title: Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification Abstract: Satellite Image Time Series (SITS) of the Earth's surface provide detailed land cover maps, with their quality in the spatial and temporal dimensions consistently improving. These image time series are i... |
Title: CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning Abstract: Recent advances in digital imaging, e.g., increased number of pixels captured, have meant that the volume of data to be processed and analyzed from these images has also increased. Deep learning algorith... |
Title: Machine Learning-Based Automated Thermal Comfort Prediction: Integration of Low-Cost Thermal and Visual Cameras for Higher Accuracy Abstract: Recent research is trying to leverage occupants' demand in the building's control loop to consider individuals' well-being and the buildings' energy savings. To that end, ... |
Title: Intelligent Spatial Interpolation-based Frost Prediction Methodology using Artificial Neural Networks with Limited Local Data Abstract: The weather phenomenon of frost poses great threats to agriculture. Since it damages the crops and plants from upstream of the supply chain, the potential impact of frosts is si... |
Title: IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation Abstract: Federated learning (FL) allows multiple medical institutions to collaboratively learn a global model without centralizing all clients data. It is difficult, if possible at all, for such a global model to commonly achieve op... |
Title: Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport Abstract: Recent advances in deep learning, such as powerful generative models and joint text-image embeddings, have provided the computational creativity community with new tools, opening new perspectives for artistic pursuits... |
Title: AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy Abstract: Evaluation of keyword spotting (KWS) systems that detect keywords in speech is a challenging task under realistic privacy constraints. The KWS is designed to only collect data when the keyword... |
Title: Predictive analytics for appointment bookings Abstract: One of the service providers in the financial service sector, who provide premium service to the customers, wanted to harness the power of data analytics as data mining can uncover valuable insights for better decision making. Therefore, the author aimed to... |
Title: Research on Domain Information Mining and Theme Evolution of Scientific Papers Abstract: In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Cross-disciplinary research results have gradually become an emerg... |
Title: Inductive Biases for Object-Centric Representations in the Presence of Complex Textures Abstract: Understanding which inductive biases could be helpful for the unsupervised learning of object-centric representations of natural scenes is challenging. We use neural style transfer to generate datasets where objects... |
Title: Active Learning Helps Pretrained Models Learn the Intended Task Abstract: Models can fail in unpredictable ways during deployment due to task ambiguity, when multiple behaviors are consistent with the provided training data. An example is an object classifier trained on red squares and blue circles: when encount... |
Title: DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning Abstract: Coreset selection, which aims to select a subset of the most informative training samples, is a long-standing learning problem that can benefit many downstream tasks such as data-efficient learning, continual learning, neural arch... |
Title: CGC: Contrastive Graph Clustering for Community Detection and Tracking Abstract: Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper, we approach this important task from graph clusterin... |
Title: Active-learning-based non-intrusive Model Order Reduction Abstract: The Model Order Reduction (MOR) technique can provide compact numerical models for fast simulation. Different from the intrusive MOR methods, the non-intrusive MOR does not require access to the Full Order Models (FOMs), especially system matric... |
Title: So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale Abstract: Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-te... |
Title: An Optimal Time Variable Learning Framework for Deep Neural Networks Abstract: Feature propagation in Deep Neural Networks (DNNs) can be associated to nonlinear discrete dynamical systems. The novelty, in this paper, lies in letting the discretization parameter (time step-size) vary from layer to layer, which ne... |
Title: Improving Information Cascade Modeling by Social Topology and Dual Role User Dependency Abstract: In the last decade, information diffusion (also known as information cascade) on social networks has been massively investigated due to its application values in many fields. In recent years, many sequential models ... |
Title: Twitter Dataset on the Russo-Ukrainian War Abstract: On 24 February 2022, Russia invaded Ukraine, also known now as Russo-Ukrainian War. We have initiated an ongoing dataset acquisition from Twitter API. Until the day this paper was written the dataset has reached the amount of 57.3 million tweets, originating f... |
Title: CBR-iKB: A Case-Based Reasoning Approach for Question Answering over Incomplete Knowledge Bases Abstract: Knowledge bases (KBs) are often incomplete and constantly changing in practice. Yet, in many question answering applications coupled with knowledge bases, the sparse nature of KBs is often overlooked. To thi... |
Title: Learning Similarity Preserving Binary Codes for Recommender Systems Abstract: Hashing-based Recommender Systems (RSs) are widely studied to provide scalable services. The existing methods for the systems combine three modules to achieve efficiency: feature extraction, interaction modeling, and binarization. In t... |
Title: A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability Abstract: Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, inclu... |
Title: Expert-Calibrated Learning for Online Optimization with Switching Costs Abstract: We study online convex optimization with switching costs, a practically important but also extremely challenging problem due to the lack of complete offline information. By tapping into the power of machine learning (ML) based opti... |
Title: Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models Abstract: We present a data-efficient framework for solving sequential decision-making problems which exploits the combination of reinforcement learning (RL) and latent variable generative models. The framework, called Ge... |
Title: Adaptive Noisy Data Augmentation for Regularized Estimation and Inference in Generalized Linear Models Abstract: We propose the AdaPtive Noise Augmentation (PANDA) procedure to regularize the estimation and inference of generalized linear models (GLMs). PANDA iteratively optimizes the objective function given no... |
Title: On Parametric Optimal Execution and Machine Learning Surrogates Abstract: We investigate optimal order execution problems in discrete time with instantaneous price impact and stochastic resilience. First, in the setting of linear transient price impact we derive a closed-form recursion for the optimal strategy, ... |
Title: MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages Abstract: We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation. MASSIVE contains 1M realistic, pa... |
Title: INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL Abstract: Model-based reinforcement learning (RL) algorithms designed for handling complex visual observations typically learn some sort of latent state representation, either explicitly or implicitly. Standard methods of this sort do not di... |
Title: Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction Abstract: Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to a... |
Title: G2GT: Retrosynthesis Prediction with Graph to Graph Attention Neural Network and Self-Training Abstract: Retrosynthesis prediction is one of the fundamental challenges in organic chemistry and related fields. The goal is to find reactants molecules that can synthesize product molecules. To solve this task, we pr... |
Title: "Flux+Mutability": A Conditional Generative Approach to One-Class Classification and Anomaly Detection Abstract: Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is at the forefront of finding new phy... |
Title: Poisons that are learned faster are more effective Abstract: Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effect... |
Title: Equity in Resident Crowdsourcing: Measuring Under-reporting without Ground Truth Data Abstract: Modern city governance relies heavily on crowdsourcing (or "co-production") to identify problems such as downed trees and power-lines. A major concern in these systems is that residents do not report problems at the s... |
Title: Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs Abstract: Learning neural ODEs often requires solving very stiff ODE systems, primarily using explicit adaptive step size ODE solvers. These solvers are computationally expensive, requiring the use of tiny step sizes for numerical stability and ... |
Title: CorrGAN: Input Transformation Technique Against Natural Corruptions Abstract: Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial perturbations and corruptions. Specifically, natural corrupt... |
Title: Topology and geometry of data manifold in deep learning Abstract: Despite significant advances in the field of deep learning in applications to various fields, explaining the inner processes of deep learning models remains an important and open question. The purpose of this article is to describe and substantiat... |
Title: GraphHop++: New Insights into GraphHop and Its Enhancement Abstract: An enhanced label propagation (LP) method called GraphHop has been proposed recently. It outperforms graph convolutional networks (GCNs) in the semi-supervised node classification task on various networks. Although the performance of GraphHop w... |
Title: Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation Abstract: For autonomous quadruped robot navigation in various complex environments, a typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller -- in... |
Title: On The Cross-Modal Transfer from Natural Language to Code through Adapter Modules Abstract: Pre-trained neural Language Models (PTLM), such as CodeBERT, are recently used in software engineering as models pre-trained on large source code corpora. Their knowledge is transferred to downstream tasks (e.g. code clon... |
Title: Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi Abstract: Transformers are the most eminent architectures used for a vast range of Natural Language Processing tasks. These models are pre-trained over a large text corpus and are meant to serve state-of-the-art ... |
Title: A Score-based Geometric Model for Molecular Dynamics Simulations Abstract: Molecular dynamics (MD) has long been the \emph{de facto} choice for modeling complex atomistic systems from first principles, and recently deep learning become a popular way to accelerate it. Notwithstanding, preceding approaches depend ... |
Title: Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning Abstract: Adverse events are a serious issue in drug development and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for... |
Title: Imbalanced Classification via a Tabular Translation GAN Abstract: When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class. We present a model based on Generative Adversarial Networks which... |
Title: Independence Testing for Bounded Degree Bayesian Network Abstract: We study the following independence testing problem: given access to samples from a distribution $P$ over $\{0,1\}^n$, decide whether $P$ is a product distribution or whether it is $\varepsilon$-far in total variation distance from any product di... |
Title: Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review Abstract: Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces wi... |
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