Latest Research in Machine Learning

Top Research Topics in Machine Learning

• Machine Learning — 246 papers
• Computation and Language — 181 papers
• Artificial Intelligence — 74 papers
• Computer Vision and Pattern Recognition — 26 papers
• Topic Modeling — 25 papers
• Natural Language Processing Techniques — 15 papers
• Distributed, Parallel, and Cluster Computing — 9 papers
• Information Retrieval — 9 papers
• Parallel Computing and Optimization Techniques — 8 papers
• Generative Adversarial Networks and Image Synthesis — 6 papers

Highest-Cited Papers

1. Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models (40 citations)
2. Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges (3 citations)
3. Mixed-Precision Quantization for Language Models: Techniques and Prospects (1 citations)
4. AI Teacher Latent Basis Reorientation
5. AI Teacher Latent Basis Reorientation
6. KD4MT: A Survey of Knowledge Distillation for Machine Translation
7. Grouped-Query Latent Sparse Attention: Compute Only Where It Matters
8. Measuring What a Human Adds to a Machine Baseline: A Problem Statement
9. Fuzzy–Bayesian Sequence-Consistent Continual Low-Rank Adaptation of Large Language Models
10. Grouped-Query Latent Sparse Attention: Compute Only Where It Matters
11. Phase-Locked Dynamic Sampling and Frequency-Domain Adaptation: Attractor-Guided Inference and Spectral Sparsity in LLMs
12. Attention Is All You Need: A Technical Review of the Transformer Architecture and Its Impact on Modern Artificial Intelligence
13. Phase-Locked Dynamic Sampling and Frequency-Domain Adaptation: Attractor-Guided Inference and Spectral Sparsity in LLMs
14. Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool
15. Wasserstein-Regularized Low-Rank Adaptation (OTLoRA): Mitigating Representation Drift and Catastrophic Forgetting in Small Language Models
16. Wasserstein-Regularized Low-Rank Adaptation (OTLoRA): Mitigating Representation Drift and Catastrophic Forgetting in Small Language Models
17. The Carried Reference Tunes a Switch–Multiplier Continuum in Gated (GLU) MLPs
18. WebRLED: A Deep Reinforcement Learning Approach for Automated Web Application Exploration
19. Cost efficient multimodal LLM deployment integrating tokenization economics MLOps and FinOps environments
20. The Carried Reference Tunes a Switch–Multiplier Continuum in Gated (GLU) MLPs