Akhil retweeted
We are grateful to all of the 17,491 reviewers who helped make #CVPR2026 possible. We are especially pleased to recognize the following Outstanding Reviewers, whose high-quality reviews (as judged by their Area Chairs) placed them among the top 5% of reviewers.
Akhil retweeted
Introducing PEGrad, a hyperparameter-free method for energy-efficient robot control. By projecting energy gradients orthogonal to task rewards, it cuts energy use by up to 64% without hurting performance. Demonstrated on DM-Control, HumanoidBench, and real Unitree GO2 robots,
Akhil retweeted
New Anthropic research paper: Scaling Monosemanticity.
The first ever detailed look inside a leading large language model.
Read the blog post here: anthropic.com/research/mappi…
ALT Title card for Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet
Akhil retweeted
New paper: The Platonic Representation Hypothesis
In which we posit that _different_ foundation models are converging to the _same_ representation of reality.
paper: arxiv.org/abs/2405.07987
website: phillipi.github.io/prh/
code: github.com/minyoungg/platoni…
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Akhil retweeted
Diffusion models rock: stable training, high quality samples, improved diversity, and moderately fast sampling. Awesome work from @prafdhar and @unixpickle showing that improved diffusion architectures + classifier guidance outperforms GANs on ImageNet.
Diffusion Models Beat GANs on Image Synthesis
Achieves 3.85 FID on ImageNet 512×512 and matches BigGAN-deep even with as few as 25 forward passes per sample, all while maintaining better coverage of the distribution.
arxiv.org/abs/2105.05233
