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UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
Machine Learning

ML/AI Engineer laid off from big tech, need your help!

I recently left a very toxic company that was taking a serious toll on my mental and physical health. I gave everything I had and it cost...

Reddit - ML Jobs · 1 min ·
Machine Learning

Trials and tribulations fine-tuning & deploying Gemma-4 [P]

Hey all, Our ML team spent some time this week getting training and deployments working for Gemma-4, and wanted to document all the thing...

Reddit - Machine Learning · 1 min ·

All Content

[2602.12624] Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps
Machine Learning

[2602.12624] Formalizing the Sampling Design Space of Diffusion-Based Generative Models via Adaptive Solvers and Wasserstein-Bounded Timesteps

This paper presents a framework for optimizing sampling in diffusion-based generative models, addressing high sampling costs through adap...

arXiv - Machine Learning · 4 min ·
[2602.12529] Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models
Machine Learning

[2602.12529] Flow-Factory: A Unified Framework for Reinforcement Learning in Flow-Matching Models

Flow-Factory presents a unified framework for reinforcement learning in flow-matching models, addressing fragmentation and complexity in ...

arXiv - Machine Learning · 3 min ·
[2602.12526] Constraint-Rectified Training for Efficient Chain-of-Thought
Llms

[2602.12526] Constraint-Rectified Training for Efficient Chain-of-Thought

The paper presents Constraint-Rectified Training (CRT), a framework designed to enhance the efficiency of Chain-of-Thought reasoning in L...

arXiv - Machine Learning · 4 min ·
[2602.12468] Continuous Diffusion Models Can Obey Formal Syntax
Llms

[2602.12468] Continuous Diffusion Models Can Obey Formal Syntax

The paper introduces a method for guiding continuous diffusion models to adhere to formal syntactic constraints, achieving high constrain...

arXiv - Machine Learning · 3 min ·
[2602.12429] Stabilizing Native Low-Rank LLM Pretraining
Llms

[2602.12429] Stabilizing Native Low-Rank LLM Pretraining

This paper presents a method for stabilizing the training of low-rank large language models (LLMs), addressing computational challenges w...

arXiv - Machine Learning · 3 min ·
[2602.12323] The Appeal and Reality of Recycling LoRAs with Adaptive Merging
Machine Learning

[2602.12323] The Appeal and Reality of Recycling LoRAs with Adaptive Merging

This article explores the effectiveness of adaptive merging methods for recycling LoRA modules in machine learning, revealing limited ben...

arXiv - Machine Learning · 4 min ·
[2602.12318] Abstractive Red-Teaming of Language Model Character
Llms

[2602.12318] Abstractive Red-Teaming of Language Model Character

This article presents a novel approach to auditing language model behavior through 'abstractive red-teaming,' identifying query types tha...

arXiv - Machine Learning · 4 min ·
[2602.12205] DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing
Machine Learning

[2602.12205] DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing

DeepGen 1.0 is a lightweight unified multimodal model designed for image generation and editing, achieving competitive performance with o...

arXiv - AI · 4 min ·
[2602.11287] HiFloat4 Format for Language Model Inference
Llms

[2602.11287] HiFloat4 Format for Language Model Inference

The paper introduces HiFloat4, a block floating-point format designed for deep learning, enhancing efficiency in language model inference...

arXiv - Machine Learning · 3 min ·
[2602.07738] Learnable Chernoff Baselines for Inference-Time Alignment
Machine Learning

[2602.07738] Learnable Chernoff Baselines for Inference-Time Alignment

The paper introduces Learnable Chernoff Baselines (LCBs) for efficient inference-time reward-guided alignment in generative models, impro...

arXiv - Machine Learning · 3 min ·
[2602.01308] Dispelling the Curse of Singularities in Neural Network Optimizations
Machine Learning

[2602.01308] Dispelling the Curse of Singularities in Neural Network Optimizations

This article explores the optimization instability in deep neural networks caused by singularities in the parametric space, proposing a m...

arXiv - Machine Learning · 4 min ·
[2511.02083] Watermarking Discrete Diffusion Language Models
Llms

[2511.02083] Watermarking Discrete Diffusion Language Models

This article presents a novel watermarking technique for discrete diffusion language models (DDLMs), addressing the need for reliable det...

arXiv - AI · 3 min ·
[2510.24803] MASPRM: Multi-Agent System Process Reward Model
Machine Learning

[2510.24803] MASPRM: Multi-Agent System Process Reward Model

The MASPRM paper introduces a novel Multi-Agent System Process Reward Model that enhances performance during inference by guiding search ...

arXiv - AI · 3 min ·
[2510.22747] Low-Resource Dialect Adaptation of Large Language Models: A French Dialect Case-Study
Llms

[2510.22747] Low-Resource Dialect Adaptation of Large Language Models: A French Dialect Case-Study

This article explores the adaptation of large language models (LLMs) for low-resource dialects, focusing on the Québec French dialect usi...

arXiv - AI · 4 min ·
[2510.26722] Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off
Machine Learning

[2510.26722] Non-Convex Over-the-Air Heterogeneous Federated Learning: A Bias-Variance Trade-off

This paper explores the challenges of heterogeneous federated learning in wireless networks, focusing on the bias-variance trade-off in n...

arXiv - Machine Learning · 4 min ·
[2510.09717] Provable Training Data Identification for Large Language Models
Llms

[2510.09717] Provable Training Data Identification for Large Language Models

This paper presents a novel approach for identifying training data in large language models, addressing issues of copyright and privacy t...

arXiv - Machine Learning · 4 min ·
[2507.16696] FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation
Llms

[2507.16696] FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation

FISHER is a proposed foundation model aimed at improving the analysis of multi-modal industrial signals, addressing the challenges posed ...

arXiv - Machine Learning · 4 min ·
[2507.03262] Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders
Llms

[2507.03262] Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders

This article investigates redundancy in multimodal large language models (MLLMs) with multiple vision encoders, revealing that more encod...

arXiv - AI · 4 min ·
[2504.20101] PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving
Llms

[2504.20101] PlanetServe: A Decentralized, Scalable, and Privacy-Preserving Overlay for Democratizing Large Language Model Serving

The paper presents PlanetServe, a decentralized overlay for scalable and privacy-preserving serving of large language models (LLMs), addr...

arXiv - AI · 4 min ·
[2502.07971] Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency
Nlp

[2502.07971] Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency

The paper presents Retreever, a tree-based hierarchical retrieval method that enhances efficiency and transparency in information retriev...

arXiv - Machine Learning · 4 min ·
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Related Topics

Machine Learning Large Language Models Generative AI Natural Language Processing Computer Vision Robotics & Embodied AI

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