GPUs, training clusters, MLOps, and deployment
We built a system, ProgramAsWeights (PAW), where a neural compiler takes a plain-English function description and produces a "neural prog...
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
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...
![[2502.07971] Hierarchical Retrieval at Scale: Bridging Transparency and Efficiency](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents Retreever, a tree-based hierarchical retrieval method that enhances efficiency and transparency in information retriev...
This paper explores compressible dynamics in deep overparameterized low-rank learning, presenting methods to enhance training efficiency ...
The paper introduces Cross-Attention Token Pruning (CATP), a method designed to enhance the accuracy of multimodal models by effectively ...
This paper presents a statistical framework for quantifying model uniqueness in heterogeneous AI ecosystems, addressing the challenge of ...
The paper discusses the need for a verification-first approach in AI-assisted peer review to prevent the collapse of the review process, ...
This article analyzes the performance and cache utilization of Data-Oriented Design (DOD) versus Object-Oriented Design (OOD) in multi-th...
The paper presents RLIE, a framework that integrates large language models (LLMs) with probabilistic rule learning to enhance rule genera...
The paper introduces Batch-CAM, a training framework for convolutional deep learning models that enhances interpretability by aligning mo...
The paper presents Invert4TVG, a novel framework for Temporal Video Grounding (TVG) that enhances action understanding through inversion ...
The paper presents CoPE-VideoLM, a novel approach that utilizes codec primitives to enhance the efficiency of video language models, sign...
The paper introduces Krites, an asynchronous caching policy for large language models (LLMs) that enhances semantic caching efficiency wh...
The paper introduces Diverging Flows, a method for detecting extrapolations in conditional generation models, enhancing safety in applica...
This paper introduces Introspective LLM, a hierarchical reinforcement learning framework that optimizes sampling temperature in large lan...
This paper presents a strategic framework for governments to decide between buying or building large language models (LLMs) for public se...
The RGAlign-Rec framework enhances proactive intent prediction in e-commerce chatbots by aligning latent query reasoning with ranking obj...
The paper presents TriGen, a novel NPU architecture designed for accelerating large language models (LLMs) through software-hardware co-d...
The paper introduces 'Theseus,' a novel method for transferring task-specific updates across different model architectures without retrai...
This article presents a deep-learning framework for registering melanoma brain metastases (MBM) to a common atlas, enhancing cohort-level...
This article examines how the availability of knowledge influences the persuasiveness of generative social agents (GSAs) in physiotherapy...
This article presents CASCA, an open-source microservice-based platform designed to enhance sustainable SLO fulfillment and service manag...
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