Search Results for author:
Found 17 papers, 2 papers with code
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization
The rapid rise of large language models (LLMs) has unlocked many applications but also underscores the challenge of aligning them with diverse values and preferences.
Iterative NLP Query Refinement for Enhancing Domain-Specific Information Retrieval: A Case Study in Career Services
Retrieving semantically relevant documents in niche domains poses significant challenges for traditional TF-IDF-based systems, often resulting in low similarity scores and suboptimal retrieval performance.
Pose Estimation and Tracking for ASIST
PETA (Pose Estimation and Tracking for ASIST) is a research effort to create a helicopter tracking system prototype without hardware installation requirements for ASIST system operators.
Computer Vision for Carriers: PATRIOT
PATRIOT (Panoramic Asset Tracking of Real-Time Information for the Ouija Tabletop) is a research effort and proposed solution to performing deck tracking with passive sensing and without the need for GPS sensors.
Scalable Unidirectional Pareto Optimality for Multi-Task Learning with Constraints
Multi-objective optimization (MOO) problems require balancing competing objectives, often under constraints.
Range-Net: A High Precision Neural SVD
For Big Data applications, computing a rank-$r$ Singular Value Decomposition (SVD) is restrictive due to the main memory requirements.
A Two-Stage Neural-Filter Pareto Front Extractor and the need for Benchmarking
The Stage-1 neural network efficiently extracts the \textit{weak} Pareto front, using Fritz-John Conditions (FJC) as the discriminator, with no assumptions of convexity on the objectives or constraints.
Tail-Net: Extracting Lowest Singular Triplets for Big Data Applications
However, a number of applications such as community detection, clustering, or bottleneck identification in large scale graph data-sets rely upon identifying the lowest singular values and the singular corresponding vectors.
SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing
We show for the first time that a two-layer autoencoder (SCA), with $2FK$ parameters ($F$ features, $K$ endmembers), achieves error metrics that are scales apart ($10^{-5})$ from previously reported values $(10^{-2})$.
A Hybrid 2-stage Neural Optimization for Pareto Front Extraction
The first stage (neural network) efficiently extracts a weak Pareto front, using Fritz-John conditions as the discriminator, with no assumptions of convexity on the objectives or constraints.
Range-Net: A High Precision Streaming SVD for Big Data Applications
Although these methods are claimed to be applicable to scientific computations due to associated tail-energy error bounds, the approximation errors in the singular vectors and values are high when the aforementioned assumption does not hold.
Extracting Optimal Solution Manifolds using Constrained Neural Optimization
However, such an approach is often restricted to a strict class of functions, deviation from which results in sub-optimal solution to the original problem.
Prevention is Better than Cure: Handling Basis Collapse and Transparency in Dense Networks
We demonstrate through carefully chosen numerical experiments that the basis collapse issue leads to the design of massively redundant networks.
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Partial Differential Equations are infinite dimensional encoded representations of physical processes.
Positive solutions for quasilinear elliptic inequalities and systems with nonlocal terms
We investigate the existence and nonexistence of positive solutions for the quasilinear elliptic inequality $L_\mathcal{A} u= -{\rm div}[\mathcal{A}(x, u, \nabla u)]\geq (I_\alpha\ast u^p)u^q$ in $\Omega$, where $\Omega\subset \mathbb{R}^N, N\geq 1,$ is an open set.
Analysis of PDEs
Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum
This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches.
High Energy Physics - Experiment High Energy Physics - Phenomenology
A Review of Machine Learning based Anomaly Detection Techniques
Intrusion detection is so much popular since the last two decades where intrusion is attempted to break into or misuse the system.
