All Updates

Page 595 of 611

February 13, 2026

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SCMP Technologyโ€ข52d ago

Enflame Nears Shanghai IPO Listing

Enflame Technology started IPO inquiry on Shanghai's Star Market. The AI chip unicorn follows hot semiconductor listings. Investor demand for domestic AI chips stays robust.

#listing#enflame#star-market
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Apple Machine Learningโ€ข52d ago

Hyperparameter Transfer Across All Scaling Axes

Apple ML extends ฮผP for hyperparameter transfer across model sizes, modules, width, depth, batch, and duration. Introduces Complete(d) Parameterisation unifying width-depth scaling. Enables optimal base hyperparameters search at small scales for large model transfer.

#research#apple-ml#mu-p
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Apple Machine Learningโ€ข52d ago

Hyperparam Transfer Across All Scales

Apple extends ฮผP for hyperparameter transfer across modules, width, depth, batch, and duration. Introduces Complete(d) Parameterisation unifying width-depth scaling. Enables optimal hypers from small to large models.

#research#apple-ml#mu-p
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Apple Machine Learningโ€ข52d ago

Faster Rates for Federated Variational Inequalities

Apple ML advances federated optimization for stochastic variational inequalities (VIs). It provides improved convergence rates closing the gap with federated convex optimization. Refined analysis shows tighter guarantees for Local Extra SGD on smooth monotone VIs.

#research#apple-ml#local-extra-sgd
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Apple Machine Learningโ€ข52d ago

Faster Rates for Federated VIs

Apple advances federated optimization for stochastic variational inequalities. Establishes improved convergence rates closing gap with convex optimization. Refined analysis boosts Local Extra SGD for smooth monotone VIs.

#research#apple-ml#local-extra-sgd
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Apple Machine Learningโ€ข52d ago

Faster Convergence in Federated VIs

This paper improves federated optimization for stochastic variational inequalities with tighter convergence rates. It refines analysis for Local Extra SGD on smooth monotone VIs, closing the gap with convex optimization bounds.

#research#apple-machine-learning#latest
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Apple Machine Learningโ€ข52d ago

Faster Convergence for Federated VIs

Apple ML paper advances federated optimization for stochastic variational inequalities. It provides improved convergence rates, closing gaps with convex optimization bounds. Refined analysis yields tighter guarantees for Local Extra SGD in smooth monotone VIs.

#research#apple-ml#extra-sgd
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Hugging Face Blogโ€ข52d ago

Custom Kernels for All from Codex & Claude

Hugging Face launches custom kernels powered by OpenAI's Codex and Anthropic's Claude. These are now available to all users on the platform. This expands access to advanced AI-driven kernel customization.

#launch#hugging-face#custom-kernels
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Hugging Face Blogโ€ข52d ago

Custom Kernels for All Users

Hugging Face introduces custom kernels powered by Codex and Claude, now available to everyone. This expands access to advanced customization options on the platform. Users can integrate these models seamlessly into their workflows.

#new-feature#hugging-face#codex-claude
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Apple Machine Learningโ€ข52d ago

Complete Hyperparameter Transfer Across Scales

Extends hyperparameter transfer from small to large models across modules, width, depth, batch, and duration. Introduces Complete(d) Parameterisation unifying width-depth scaling, building on ฮผP.

#research#apple-machine-learning#latest
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Apple Machine Learningโ€ข52d ago

Complete Hyperparameter Transfer for Scaling

Apple ML extends ฮผP parameterisations with Complete(d) Parameterisation for hyperparameter transfer. Covers scaling across modules, width, depth, batch size, and duration. Enables optimal hyperparameter search on small models for transfer to large-scale ones.

#research#apple-ml#mu-p
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Apple Machine Learningโ€ข52d ago

Cadmus: Low-Cost Program Synthesis System

Apple ML introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup enables controlled experiments bypassing issues with large LLMs like OOD challenges and high resource demands.

#research#apple-ml#cadmus
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Apple Machine Learningโ€ข52d ago

Cadmus Enables Cheap Program Synthesis Experiments

Apple Machine Learning introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup allows controlled experimentation without the complexities of large LLMs.

#research#apple#cadmus
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Apple Machine Learningโ€ข52d ago

Cadmus: Cheap Program Synthesis System

Apple unveils Cadmus, a small-scale system for autoregressive program synthesis. It features an integer VM, diverse program dataset, and transformer model trained under $200 compute. Enables controlled experiments bypassing LLM challenges like OOD and tokenization.

#research#apple-ml#cadmus
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Apple Machine Learningโ€ข52d ago

Cadmus: Affordable Autoregressive Program Synthesis

Apple ML introduces Cadmus, a small-scale system for autoregressive program synthesis. It features an integer virtual machine, a dataset of diverse true programs, and a transformer model trained for under $200 compute. This setup enables controlled experiments avoiding LLM pitfalls like OOD issues and high compute demands.

#research#apple-ml#cadmus

February 12, 2026

Page 595 of 611