Quantile-based Maximum Likelihood Training for Outlier Detection
Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives for outliers, posing critical concerns in applications like...
View ArticleZero-Shot Refinement of Buildings' Segmentation Models using SAM
Foundation models have excelled in various tasks but are often evaluated on general benchmarks. The adaptation of these models for specific domains, such as remote sensing imagery, remains an...
View ArticlePCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph...
Jet tagging is a classification problem in high-energy physics experiments that aims to identify the collimated sprays of subatomic particles, jets, from particle collisions and tag them to their...
View ArticleEvaluation of Reinforcement Learning Techniques for Trading on a Diverse...
This work seeks to answer key research questions regarding the viability of reinforcement learning over the S&P 500 index. The on-policy techniques of Value Iteration (VI) and...
View ArticleCertifying LLM Safety against Adversarial Prompting
Large language models (LLMs) are vulnerable to adversarial attacks that add malicious tokens to an input prompt to bypass the safety guardrails of an LLM and cause it to produce harmful content. In...
View ArticleOn Penalty Methods for Nonconvex Bilevel Optimization and First-Order...
In this work, we study first-order algorithms for solving Bilevel Optimization (BO) where the objective functions are smooth but possibly nonconvex in both levels and the variables are restricted to...
View ArticleAcoustic-to-articulatory inversion for dysarthric speech: Are pre-trained...
Acoustic-to-articulatory inversion (AAI) involves mapping from the acoustic to the articulatory space. Signal-processing features like the MFCCs, have been widely used for the AAI task. For subjects...
View ArticleBayesian deep learning for cosmic volumes with modified gravity
The new generation of galaxy surveys will provide unprecedented data allowing us to test gravity at cosmological scales. A robust cosmological analysis of the large-scale structure demands exploiting...
View ArticleNatural Quantum Monte Carlo Computation of Excited States
We present a variational Monte Carlo algorithm for estimating the lowest excited states of a quantum system which is a natural generalization of the estimation of ground states. The method has no free...
View ArticleKnowledge Transfer from High-Resource to Low-Resource Programming Languages...
Over the past few years, Large Language Models of Code (Code LLMs) have started to have a significant impact on programming practice. Code LLMs are also emerging as building blocks for research in...
View ArticleDetecting and Preventing Hallucinations in Large Vision Language Models
Instruction tuned Large Vision Language Models (LVLMs) have significantly advanced in generalizing across a diverse set of multi-modal tasks, especially for Visual Question Answering (VQA). However,...
View ArticleFollow Anything: Open-set detection, tracking, and following in real-time
Tracking and following objects of interest is critical to several robotics use cases, ranging from industrial automation to logistics and warehousing, to healthcare and security. In this paper, we...
View ArticleDesign Space Exploration on Efficient and Accurate Human Pose Estimation from...
Human Pose Estimation (HPE) to assess human motion in sports, rehabilitation or work safety requires accurate sensing without compromising the sensitive underlying personal data. Therefore, local...
View ArticleAdaptive Proximal Gradient Method for Convex Optimization
In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on making these algorithms...
View ArticleSoft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
Dense retrieval (DR) converts queries and documents into dense embeddings and measures the similarity between queries and documents in vector space. One of the challenges in DR is the lack of...
View ArticleHarpa: High-Rate Phase Association with Travel Time Neural Fields
Our understanding of regional seismicity from multi-station seismograms relies on the ability to associate arrival phases with their originating earthquakes. Deep-learning-based phase detection now...
View ArticleSampling the lattice Nambu-Goto string using Continuous Normalizing Flows
Effective String Theory (EST) represents a powerful non-perturbative approach to describe confinement in Yang-Mills theory that models the confining flux tube as a thin vibrating string. EST...
View ArticleHyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance...
Open World Object Detection (OWOD) is a challenging and realistic task that extends beyond the scope of standard Object Detection task. It involves detecting both known and unknown objects while...
View ArticleUnderstanding quantum machine learning also requires rethinking generalization
Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we show that traditional...
View ArticleSqueeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A...
We present a new dataset condensation framework termed Squeeze, Recover and Relabel (SRe$^2$L) that decouples the bilevel optimization of model and synthetic data during training, to handle varying...
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