:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Saxena, Aayush, Bishwas, Arit Kumar, Mishra, Ayush Ashok, Armstrong, Ryan
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2407.15904
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards Resource-Efficient Multimodal Intelligence: Learned Routing among Specialized Expert Models
by: Saini, Mayank, et al.
Published: (2025)

One Supervisor, Many Modalities: Adaptive Tool Orchestration for Autonomous Queries
by: Saini, Mayank, et al.
Published: (2026)

Investigation into the Potential of Parallel Quantum Annealing for Simultaneous Optimization of Multiple Problems: A Comprehensive Study
by: Bishwas, Arit Kumar, et al.
Published: (2024)

ODD: Overlap-aware Estimation of Model Performance under Distribution Shift
by: Mishra, Aayush, et al.
Published: (2025)

Strategic Roadmap for Quantum- Resistant Security: A Framework for Preparing Industries for the Quantum Threat
by: Bishwas, Arit Kumar, et al.
Published: (2024)

Reducing Complexity for Quantum Approaches in Train Load Optimization
by: Tang, Zhijie, et al.
Published: (2026)

A Comprehensively Adaptive Architectural Optimization-Ingrained Quantum Neural Network Model for Cloud Workloads Prediction
by: Kumar, Jitendra, et al.
Published: (2025)

Molecular unfolding formulation with enhanced quantum annealing approach
by: Bishwas, Arit Kumar, et al.
Published: (2024)

Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization
by: Mandal, Bishwas, et al.
Published: (2024)

Energy Efficient Protein Language Models: Leveraging Small Language Models with LoRA for Controllable Protein Generation
by: Shah, Aayush, et al.
Published: (2024)

Ensemble Deep Learning Models for Early Detection of Meningitis in ICU: Multi-center Study
by: Ouyang, Han, et al.
Published: (2025)

A Comprehensive Performance Comparison of Traditional and Ensemble Machine Learning Models for Online Fraud Detection
by: Khekare, Ganesh, et al.
Published: (2025)

Benchmarking Traditional Machine Learning and Deep Learning Models for Fault Detection in Power Transformers
by: Saravanan, Bhuvan, et al.
Published: (2025)

Unsupervised Continual Learning for Amortized Bayesian Inference
by: Mishra, Aayush, et al.
Published: (2026)

A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
by: Feng, Kaidong, et al.
Published: (2026)

Bridging Compressed Image Latents and Multimodal Large Language Models
by: Kao, Chia-Hao, et al.
Published: (2024)

Model Provenance Testing for Large Language Models
by: Nikolic, Ivica, et al.
Published: (2025)

AA-SVD : Anchored and Adaptive SVD for Large Language Model Compression
by: Sinha, Atul Kumar, et al.
Published: (2026)

Comparative analysis of diverse methodologies for portfolio optimization leveraging quantum annealing techniques
by: Tang, Zhijie, et al.
Published: (2024)

Do pretrained Transformers Learn In-Context by Gradient Descent?
by: Shen, Lingfeng, et al.
Published: (2023)

A Comprehensive Evaluation on Quantization Techniques for Large Language Models
by: Liu, Yutong, et al.
Published: (2025)

Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events
by: Khot, Ayush, et al.
Published: (2024)

DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings
by: Mishra, Aditya, et al.
Published: (2023)

Radio: Rate-Distortion Optimization for Large Language Model Compression
by: Young, Sean I.
Published: (2025)

Large Language Model Compression with Global Rank and Sparsity Optimization
by: Zhou, Changhai, et al.
Published: (2025)

Learn Globally, Speak Locally: Bridging the Gaps in Multilingual Reasoning
by: Hwang, Jaedong, et al.
Published: (2025)

EdgeJury: Cross-Reviewed Small-Model Ensembles for Truthful Question Answering on Serverless Edge Inference
by: Kumar, Aayush
Published: (2025)

FineZip : Pushing the Limits of Large Language Models for Practical Lossless Text Compression
by: Mittu, Fazal, et al.
Published: (2024)

Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
by: Tran, Hoang, et al.
Published: (2024)

PalmBench: A Comprehensive Benchmark of Compressed Large Language Models on Mobile Platforms
by: Li, Yilong, et al.
Published: (2024)

Alignment Adapter to Improve the Performance of Compressed Deep Learning Models
by: Rai, Rohit Raj, et al.
Published: (2026)

ISO-Bench: Can Coding Agents Optimize Real-World Inference Workloads?
by: Nangia, Ayush, et al.
Published: (2026)

Improving the Accuracy of Amortized Model Comparison with Self-Consistency
by: Kucharský, Šimon, et al.
Published: (2025)

Improving the Accuracy of Amortized Model Comparison with Self-Consistency
by: Kucharský, Šimon, et al.
Published: (2025)

Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models
by: Qi, Zhen, et al.
Published: (2024)

A Systematic Study of Compression Ordering for Large Language Models
by: Chhawri, Shivansh, et al.
Published: (2025)

GWT: Scalable Optimizer State Compression for Large Language Model Training
by: Wen, Ziqing, et al.
Published: (2025)

Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization
by: Zhao, Jie, et al.
Published: (2025)

Steered LLM Activations are Non-Surjective
by: Mishra, Aayush, et al.
Published: (2026)

Timeseries Foundation Models for Mobility: A Benchmark Comparison with Traditional and Deep Learning Models
by: Graser, Anita
Published: (2025)