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Ke Li 🍁

@KL_Div • 7,393 subscribers

Associate Professor and Canada CIFAR AI Chair @SFU @AmiiThinks. Ph.D. from @Berkeley_EECS and Bachelor's from @UofTCompSci. Formerly @GoogleAI and @the_IAS.

Shorts

Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really need all this complexity? We design a small and simple one-step model, ROMS-IMLE, which attains an FID of 2.56 on ImageNet. Joint w/ Chirag Vashist 1/6

Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really need all this complexity? We design a small and simple one-step model, ROMS-IMLE, which attains an FID of 2.56 on ImageNet. Joint w/ Chirag Vashist 1/6

37,065 görüntüleme

LLMs require more GPU memory as they generate longer responses. Can we make GPU memory constant without significantly sacrificing accuracy? IceCache is a new method for managing KV caches that leverages Dynamic Continuous Indexing (DCI) to efficiently group and retrieve tokens by semantics. Joint work w/ Yuzhen Mao, Qitong Wang and Martin Ester. For details, check out the links below.

LLMs require more GPU memory as they generate longer responses. Can we make GPU memory constant without significantly sacrificing accuracy? IceCache is a new method for managing KV caches that leverages Dynamic Continuous Indexing (DCI) to efficiently group and retrieve tokens by semantics. Joint work w/ Yuzhen Mao, Qitong Wang and Martin Ester. For details, check out the links below.

21,163 görüntüleme

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Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really need all this complexity? We design a small and simple one-step model, ROMS-IMLE, which attains an FID of 2.56 on ImageNet. Joint w/ Chirag Vashist 1/6

Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really need all this complexity? We design a small and simple one-step model, ROMS-IMLE, which attains an FID of 2.56 on ImageNet. Joint w/ Chirag Vashist 1/6

37,065 görüntüleme • 1 ay önce

Diffusion and flow matching-based robot planners are slow and generate noisy and jerky trajectories. Delighted to share our ICRA 2026 paper, which leverages IMLE to improve planning frequency 19-fold from 4.3 Hz to 83 Hz and reduces jerk by 38% relative to flow matching. Joint work w/ Grayson Lee, Minh Bui, Shuzi Zhou, Yankai Li and Mo Chen. (1/7)

Diffusion and flow matching-based robot planners are slow and generate noisy and jerky trajectories. Delighted to share our ICRA 2026 paper, which leverages IMLE to improve planning frequency 19-fold from 4.3 Hz to 83 Hz and reduces jerk by 38% relative to flow matching. Joint work w/ Grayson Lee, Minh Bui, Shuzi Zhou, Yankai Li and Mo Chen. (1/7)

102,877 görüntüleme • 4 ay önce

How can we train generative models on 100 examples without pretraining on large datasets? Presenting Adaptive IMLE, which outperforms both GANs and diffusion models. Details at Work w/ Mehran Aghabozorgi and Shichong Peng. Come by our #ICML2023 poster Wed AM 1/2

How can we train generative models on 100 examples without pretraining on large datasets? Presenting Adaptive IMLE, which outperforms both GANs and diffusion models. Details at Work w/ Mehran Aghabozorgi and Shichong Peng. Come by our #ICML2023 poster Wed AM 1/2

15,387 görüntüleme • 3 yıl önce

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