Latent variable models represent hidden structure in observational data.To account for the distribution of the observational data changing over time, space or some other covariate, we need generalizations of latent variable models that explicitly capture this dependency on the covariate. A variety of such generalizations has been proposed for latent variable models based on the Dirichlet process. We address dependency on covariates in binary latent feature models, by introducing a dependent Indian buffet process. The model generates, for […]

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TL;DR: We collected 87,871 observations showing AI epistemic self-assessment produces consistent, calibratable measurements. No consciousness claims required. The Conflation Problem When people hear “AI assesses its uncertainty,” they assume it requires consciousness or introspection. It doesn’t. Functional Measurement Phenomenological Introspection “Rate your knowledge 0-1” “Are you aware of your states?” Evaluating context window Accessing inner experience Thermometer measuring temp Thermometer feeling hot A thermometer doesn’t need to feel hot. An LLM evaluating knowledge state is doing the same […]

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arXiv:2601.00216v1 Announce Type: new Abstract: In medicine, large language models (LLMs) increasingly rely on retrieval-augmented generation (RAG) to ground outputs in up-to-date external evidence. However, current RAG approaches focus primarily on performance improvements while overlooking evidence-based medicine (EBM) principles. This study addresses two key gaps: (1) the lack of PICO alignment between queries and retrieved evidence, and (2) the absence of evidence hierarchy considerations during reranking. We present a generalizable strategy for adapting EBM to graph-based RAG, integrating […]

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We present a new operator theoretic framework for analysis of complex systems with intrinsic subdivisions into components, taking the form of "residuals" in general, and "telescoping energy residuals" in particular. We prove new results which yield admissibility/effectiveness, and new a priori bounds on energy residuals. Applications include infinite-dimensional Kaczmarz theory for $λ_{n}$-relaxed variants, and $λ_{n}$-effectiveness. And we give applications of our framework to generalized machine learning algorithms, greedy Kernel Principal Component Analysis (KPCA), proving explicit convergence results, residual […]

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In most enterprise Databricks environments (like in MSC or large analytics ecosystems), system tables such as system.jobrunlogs or system.cluster_events may be restricted or disabled due to security or governance policies. However, tracking cluster utilization and cost is crucial for : Understanding how efficiently jobs use compute Identifying idle clusters or cost leaks Forecasting infrastructure budget Building custom cost dashboards This blog demonstrates a step-by-step approach to compute cluster utilization and cost using only Databricks REST APIs — no […]

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Hey guys, I’m working on optimizing inference for transformer models and keep seeing memory bandwidth become the bottleneck well before compute, especially once context length gets past ~8k tokens. A few questions for for teams running LLaMA / Mistral / similar models in production: Is KV cache memory your limiting factor at longer context? Do you hit HBM limits or throughput collapse first? What have you tried so far (quantization, FlashAttention variants, batching tweaks, offloading, etc.)? What tradeoffs […]

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How would you rate each of these journals for GenAI, NeuroSymbolicAI, DL/ML papers: AIJ, JAIR, JETAI, TMLR, JMLR, ML Springer, The European Journal on Artificial Intelligence? submitted by /u/NoFormal8277 [link] [comments]

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