Book Review: ‘Deeply Dave,’ by Michael Grover, and ‘Schoolbot 9000,’ by Sam Hepburn
nytimes.com·1d
🧩programming
Voxtral
mistral.ai·3d·
Discuss: Hacker News
🧩programming
Quoting Paul Kedrosky
simonwillison.net·10h
🎨Graphics Programming
To the Moon and quack: Books in brief
nature.com·1d
🧩programming
Compliance Minimization via Physics-Informed Gaussian Processes
arxiv.org·4d
🎨Graphics Programming
Draw an Ugly Person An Exploration of Generative AIs Perceptions of Ugliness
arxiv.org·2d
🧩programming
Dual form Complementary Masking for Domain-Adaptive Image Segmentation
arxiv.org·2d
🎨Graphics Programming
Multiplicative Modular Nim (MuM)
arxiv.org·4d
🔱plan 9
Warm Starts Accelerate Generative Modelling
arxiv.org·4d
🎨Graphics Programming
ViSP: A PPO-Driven Framework for Sarcasm Generation with Contrastive Learning
arxiv.org·4d
🧩programming
How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study
arxiv.org·3d
🧩programming
Learning to Quantize and Precode in Massive MIMO Systems for Energy Reduction: a Graph Neural Network Approach
arxiv.org·3d
🧩programming
Transformer-Based Person Identification via Wi-Fi CSI Amplitude and Phase Perturbations
arxiv.org·1d
🌐Network Protocols
Leveraging Large Language Models for Classifying App Users' Feedback
arxiv.org·5d
🧩programming
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach
arxiv.org·4d
🧩programming
Divide-Then-Rule: A Cluster-Driven Hierarchical Interpolator for Attribute-Missing Graphs
arxiv.org·3d
🧩programming
A Neural Network Model of Complementary Learning Systems: Pattern Separation and Completion for Continual Learning
arxiv.org·3d
🧩programming
CompressedVQA-HDR: Generalized Full-reference and No-reference Quality Assessment Models for Compressed High Dynamic Range Videos
arxiv.org·2d
🎨Graphics Programming
Subjective Evaluation Profile Analysis of Science Fiction Short Stories and its Critical-Theoretical Significance
arxiv.org·2d
🧩programming
Time series classification of satellite data using LSTM networks: an approach for predicting leaf-fall to minimize railroad traffic disruption
arxiv.org·2d
🧩programming