Low-Level Hacks
📊Profiling Tools
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Don’t Just Normalize, Batch Normalize! A Guide to Stable Neural Networks
pub.towardsai.net·1d
📊Gradient Accumulation
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Efficiency vs. Alignment: Investigating Safety and Fairness Risks in Parameter-Efficient Fine-Tuning of LLMs
arxiv.org·12h
🔄ONNX
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Enhancing riverine cyanobacterial bloom prediction: A hybrid deep learning approach combining wavelet decomposition, double-layer LSTM, ARIMA, and residual comp...
sciencedirect.com·1h
📊Gradient Accumulation
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Bayesian Natural Gradient Fine-Tuning of CLIP Models via Kalman Filtering
arxiv.org·12h
📊Gradient Accumulation
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GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
arxiv.org·12h
🔄ONNX
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Few-Shot Multimodal Medical Imaging: A Theoretical Framework
arxiv.org·12h
🏎️TensorRT
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Tackling the Kidnapped Robot Problem via Sparse Feasible Hypothesis Sampling and Reliable Batched Multi-Stage Inference
arxiv.org·12h
📊Gradient Accumulation
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Benchmarking individual tree segmentation using multispectral airborne laser scanning data: the FGI-EMIT dataset
arxiv.org·12h
🏎️TensorRT
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LLM-Centric RAG with Multi-Granular Indexing and Confidence Constraints
arxiv.org·1d
🛠Ml-eng
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Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet
arxiv.org·12h
🧠BF16
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Self-Improving Vision-Language-Action Models with Data Generation via Residual RL
arxiv.org·12h
🏎️TensorRT
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Enhancing Diffusion-based Restoration Models via Difficulty-Adaptive Reinforcement Learning with IQA Reward
arxiv.org·12h
📉Model Quantization
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Accelerated Dielectric Barrier Coating Optimization via Multi-Modal Data Fusion & Bayesian Hyperparameter Tuning
⏱️Benchmarking
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A filtering scheme for confocal laser endomicroscopy (CLE)-video sequences for self-supervised learning
arxiv.org·12h
🧮cuDNN
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Diagnosing Hallucination Risk in AI Surgical Decision-Support: A Sequential Framework for Sequential Validation
arxiv.org·12h
🔄ONNX
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VLM6D: VLM based 6Dof Pose Estimation based on RGB-D Images
arxiv.org·12h
🧮cuDNN
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