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X-WR-RELCALID:efc09d74-9c93-479e-a94f-485231ddccde
X-WR-TIMEZONE:America/Vancouver
X-WR-CALNAME:Efficient Inference of Mixture-of-Experts (MoE)-based Large Mo
 dels with Theoretical Guarantees - Meng Wang\, Professor\, Rensselaer Poly
 technic Institute
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TZID:America/Vancouver
TZUNTIL:20270314T100000Z
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DTSTART:20241103T020000
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TZOFFSETTO:-0800
RDATE:20251102T020000
RDATE:20261101T020000
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DTSTAMP:20260416T104102Z
CLASS:PUBLIC
CREATED:20250709T194331Z
DESCRIPTION:Abstract: Mixture-of-Experts (MoE) architectures have emerged a
 s a powerful paradigm for scaling large models by routing inputs to specia
 lized subnetworks (experts)\, achieving impressive performance with reduce
 d computation during training. However\, efficient inference of MoE models
  remains challenging due to memory and computational overhead\, especially
  when deployed in resource-constrained environments. In this talk\, I will
  first introduce a provably efficient expert pruning method for fine-tuned
  MoE models\, which preserves test-time accuracy by pruning experts with m
 inimal change in router…
DTSTART;TZID=America/Vancouver:20250715T140000
DTEND;TZID=America/Vancouver:20250715T150000
LAST-MODIFIED:20250709T195414Z
LOCATION:UBC Vancouver Campus\, Fried Kaiser (KAIS) building\, Room 2020/20
 30\, 2332 Main Mall
SUMMARY:Efficient Inference of Mixture-of-Experts (MoE)-based Large Models 
 with Theoretical Guarantees - Meng Wang\, Professor\, Rensselaer Polytechn
 ic Institute
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/efficient-inference-mixture-experts-moe-base
 d-large-models-theoretical-guarantees-meng-wang
END:VEVENT
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