Muhammad Athar Ganaie @matharg7
Joined December 2023-
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We will be presenting Self-Distilled Sparse Drafters at #ICML this afternoon at the Efficient Systems for Foundation Models workshop. Come chat with us about acclerating speculative decoding! 🚀 @CerebrasSystems @UCalgaryML
I am excited to share that I will present two of my works at #ICML2025 workshops! If you are interested in AI security and XAI, feel free to come by and chat - I'm looking forward to all feedbacks and discussions!
Paper 📄: openreview.net/forum?id=0zKHU… Poster session: 10:15-11:15 am & 4:45 - 5:30 pm. Joint work w/ @EkanshSh @rahulgk @yanii
Excited to be presenting our work at ICML next week! If you're interested in loss landscapes, weight symmetries, or sparse training, come check out our poster — we'd love to chat. 📍 East Exhibition Hall A-B, #E-2106. 📅 Tue, July 15 | 🕚 11:00 a.m. – 1:30 p.m PDT.
Excited to be presenting our work at ICML next week! If you're interested in loss landscapes, weight symmetries, or sparse training, come check out our poster — we'd love to chat. 📍 East Exhibition Hall A-B, #E-2106. 📅 Tue, July 15 | 🕚 11:00 a.m. – 1:30 p.m PDT.
@UCalgaryML will be at #ICML2025 in Vancouver🇨🇦 next week: our lab has 6 different works being presented by 5 students across both the main conference & 6 workshops! We are also recruiting fully-funded ML PhD/MSc students🧑🎓If attending, reach out to me on Whova app to meet 📅
🎉 Excited to share our latest work has been accepted at #ICML2025. We explain how Lottery Ticket masks can be extended to arbitrary random initializations by leveraging permutations of weight symmetries achieving strong generalization. Check out the details:…
🎉 Excited to share our latest work has been accepted at #ICML2025. We explain how Lottery Ticket masks can be extended to arbitrary random initializations by leveraging permutations of weight symmetries achieving strong generalization. Check out the details:…
✨Our paper is now officially published in Transactions on Machine Learning Research (TMLR)! We explore how knowledge #distillation (KD) impacts fairness & bias in AI models, across both group and individual fairness.
If you have experience with sparsity in LLMs, consider joining as a reviewer for our workshop at @iclr_conf
If you have experience with sparsity in LLMs, consider joining as a reviewer for our workshop at @iclr_conf
Excited to share our work with the community. Thanks @CohereForAI for hosting!
Excited to share our work with the community. Thanks @CohereForAI for hosting!
Lightening talk by Muhammad Athar Ganaie! Closer look at Sparse Training in Deep RL!
I'm proud that the @UCalgaryML lab will have 6 different works being presented by 6 students across #NeurIPS2024, in workshops (@unireps, @WiMLworkshop, MusiML) and the main conference! 🎉 Hope to see you at our posters/talks 🤓, full schedule at calgaryml.com 🧵(1/4)
✨Our new @unireps paper tries to answer why the Lottery Ticket Hypothesis (LTH) fails to work for different random inits through the lens of weight-space symmetry. We improve the transferability of LTH masks to new random inits leveraging weight symmetries. 🧵(1/6)
Our workshop proposal on sparsity in LLMs, including quantization, MoEs, etc. was accepted at @iclr_conf 2025! 🎉 openreview.net/forum?id=AqpDR… Collaboration with @TianlongChen4 @utkuevci @BerivanISIK @Shiwei_Liu66 @adnan_ahmad1306 Alexandra Novak and @PandaAshwinee
Congratulations @adnan_ahmad1306 on receiving the @RBCBorealis Fellowship! Adnan is a PhD student in the @UCalgaryML lab at @SchulichENGG/@UCalgary, and @VectorInst, co-supervised by @rahulgk.
Congratulations @adnan_ahmad1306 on receiving the @RBCBorealis Fellowship! Adnan is a PhD student in the @UCalgaryML lab at @SchulichENGG/@UCalgary, and @VectorInst, co-supervised by @rahulgk.
Happy to share our work on Knowledge Distillation temperature🌡️ and fairness⚖️. Must-read for AI practitioners given the prevalence of distilled models! KD is crucial in deploying AI, e.g. limited hardware, real-time applications 🙏@sarahookr @mikelasby for feedback! @UCalgaryML
Happy to share our work on Knowledge Distillation temperature🌡️ and fairness⚖️. Must-read for AI practitioners given the prevalence of distilled models! KD is crucial in deploying AI, e.g. limited hardware, real-time applications 🙏@sarahookr @mikelasby for feedback! @UCalgaryML
Proud to share that @mikelasby’s collaborative work with @_anna_go @utkuevci @MihaiCNica will be at #ICLR2024! I believe this work represents a significant step towards SOTA sparse training methods (i.e. Dynamic Sparse Training) having real-world impact @UCalgaryML @SchulichENGG
Proud to share that @mikelasby’s collaborative work with @_anna_go @utkuevci @MihaiCNica will be at #ICLR2024! I believe this work represents a significant step towards SOTA sparse training methods (i.e. Dynamic Sparse Training) having real-world impact @UCalgaryML @SchulichENGG
Knowledge #distillation is a widely used model compression method. We explore the nuanced impact of temperature on distilled models' #fairness ⚖️. Our findings show distilled students are less fair than their teachers at typical temps, but can be more fair in some instances…🧵👇
"Dynamic Sparse Training with Structured Sparsity" (openreview.net/forum?id=kOBkx…) was accepted at ICLR 2024! DST methods learn state-of-the-art sparse masks, but accelerating DNNs with unstructured masks is difficult. SRigL learns structured masks, improving real-world CPU/GPU timings
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