Explore More, Learn Better: Parallel MLLM Embeddings under Mutual Information Minimization
arxiv.org·2h
🔢Embeddings
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How Transformer Models Detect Anomalies in System Logs
hackernoon.com·13h
⛰️Gradient Descent
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Enhanced Richardson Extrapolation via Adaptive Kernel Regression and Uncertainty Quantification
dev.to·17h·
Discuss: DEV
⛰️Gradient Descent
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Benchmarking Generative AI Against Bayesian Optimization for Constrained Multi-Objective Inverse Design
arxiv.org·2h
⛰️Gradient Descent
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Uncertain node-state PI-DBN: A novel framework for predictive modeling of real-time blowout risk in deepwater drilling
sciencedirect.com·16h
🔗Markov Chains
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Masked Softmax Layers in PyTorch
mcognetta.github.io·15h·
Discuss: Hacker News
⛰️Gradient Descent
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Anatomically Constrained Transformers for Echocardiogram Analysis
arxiv.org·2h
🗺️UMAP
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Dynamic Model Selection for Trajectory Prediction via Pairwise Ranking and Meta-Features
arxiv.org·2h
⛰️Gradient Descent
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Bayesian Natural Gradient Fine-Tuning of CLIP Models via Kalman Filtering
arxiv.org·2h
⛰️Gradient Descent
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Fast, Scalable LDA in C++ with Stochastic Variational Inference
github.com·16h·
Discuss: r/cpp
🔢Embeddings
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What Are Auto-regressive Models? A Deep Dive and Typical Use Cases
blog.pangeanic.com·18h
⛰️Gradient Descent
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Weak-To-Strong Generalization
lesswrong.com·2d
📈Linear Models
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Gated DeltaNet (Linear Attention variant in Qwen3-Next and Kimi Linear)
sebastianraschka.com·1d·
Discuss: r/LLM
⛰️Gradient Descent
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Variational Data-Consistent Assimilation
arxiv.org·2h
🔥ComplexHeatmap
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Spatial Secrets: Unleashing Language Models with Unexpected Masking by Arvind Sundararajan
dev.to·2h·
Discuss: DEV
🔢Embeddings
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Don’t Just Normalize, Batch Normalize! A Guide to Stable Neural Networks
pub.towardsai.net·1d
⛰️Gradient Descent
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Connectivity Structure and Dynamics of Nonlinear Recurrent Neural Networks
journals.aps.org·7h
⛰️Gradient Descent
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Probabilistic Robustness for Free? Revisiting Training via a Benchmark
arxiv.org·2h
⛰️Gradient Descent
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