Alper Canberk (@alpercanbe) 's Twitter Profile
Alper Canberk

@alpercanbe

cs @columbia, sensorimotor intelligence @ - | prev videogen @snap

ID: 1158094343112142848

linkhttp://alpercanberk.github.io calendar_today04-08-2019 19:16:22

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403 Followers

529 Following

Huy Ha (@haqhuy) 's Twitter Profile Photo

Excited to announce the 1st Workshop on Robot Hardware-Aware Intelligence @ #RSS2025 in LA! We’re bringing together interdisciplinary researchers exploring how to unify hardware design and intelligent algorithms in robotics! Full info: rss-hardware-intelligence.github.io Robotics: Science and Systems

Excited to announce the 1st Workshop on Robot Hardware-Aware Intelligence @ #RSS2025 in LA! We’re bringing together interdisciplinary researchers exploring how to unify hardware design and intelligent algorithms in robotics! Full info: rss-hardware-intelligence.github.io

<a href="/RoboticsSciSys/">Robotics: Science and Systems</a>
Omar Khattab (@lateinteraction) 's Twitter Profile Photo

For many empirical CS/AI research problems: If your project doesn't start with a rather lengthy engineering phase where you just get something to work as well as it can without novelty, you're likely just setting yourself up for non-additive success thanks to weak baselines.

Tanishq Mathew Abraham, Ph.D. (@iscienceluvr) 's Twitter Profile Photo

Practical Efficiency of Muon for Pretraining "We demonstrate that Muon, the simplest instantiation of a second-order optimizer, explicitly expands the Pareto frontier over AdamW on the compute-time tradeoff. We find that Muon is more effective than AdamW in retaining data

Practical Efficiency of Muon for Pretraining

"We demonstrate that Muon, the simplest instantiation of a second-order optimizer, explicitly expands the Pareto frontier over AdamW on the compute-time tradeoff. We find that Muon is more effective than AdamW in retaining data
Jay (@jayendra_ram) 's Twitter Profile Photo

Over the last few months, the team at hud has made a lot of evals and environments. When we first started, we ran into a lot of problems: 1) Hosting CUA evals is annoying 2) Creating RL environments and problems is hard 3) Reviewing trajectories was super tedious 4) There