#relu — HeatPulse — HeatPulse

LIVE PULSE

4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src4.0 Anthropic CEO Amodei calls for slower AI development and shared safety rules — 11 src2.2 Agility Robotics unveils Digit 5 humanoid for warehouses and factories — 2 src2.0 Apple ships rebuilt Siri with Google Gemini, but not in the EU — 2 src1.8 Siri AI in macOS 27 Golden Gate: FAQ, Germany availability, privacy questions — 2 src1.4 Sam Altman says OpenAI will not go public in 2026, citing AI safety concerns — 5 src1.1 OpenAI contractors review real ChatGPT conversations to rate responses, report says — 2 src1.1 Anthropic data retention policy prompts firms to limit Claude use for sensitive work — 1 src1.1 Study examines issue bias in LLMs used as writing assistants before Swedish 2026 election — 1 src1.1 Study Audits Misalignment in Multi-Modal World Models — 1 src1.1 Retrieval-Grounded Reasoning Approach Proposed for Universal Multimodal Embeddings — 1 src

#relu

2 curated events

papersTODAY 04:00 UTC

ReLU Networks Shown to Approximate Smooth Functionals on Hilbert Space

A new arXiv preprint analyzes how well deep ReLU networks uniformly approximate smooth scalar-valued functionals defined on an infinite-dimensional separable Hilbert space. The author expresses functional inputs as expansions over a basis and derives error bounds, showing how the required network complexity scales with the decay of the input's coefficients. The results characterize a dimensional-decay effect along with accompanying error analysis.

research/approximation-theory/relu/theory

papersSEP 11 04:00 UTC

Lower Bounds Derived for Shallow ReLU^k Network Approximation on the Sphere

A new arXiv paper proves two distinct lower bounds on how well shallow ReLU^k neural networks can approximate functions defined on the unit sphere. For any fixed choice of inner network parameters, the best L2 approximation error is bounded from below, with a second result addressing a related configuration-dependent setting. The work characterizes fundamental limits of shallow architectures with higher-order ReLU activations rather than proposing a new method.

theory/neural-networks/approximation/relu