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DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
Hussein Hazimeh · Zhe Zhao · Aakanksha Chowdhery · Maheswaran Sathiamoorthy · Yihua Chen · Rahul Mazumder · Lichan Hong · Ed Chi

Fri Dec 10 08:30 AM -- 10:00 AM (PST) @

The Mixture-of-Experts (MoE) architecture is showing promising results in improving parameter sharing in multi-task learning (MTL) and in scaling high-capacity neural networks. State-of-the-art MoE models use a trainable "sparse gate'" to select a subset of the experts for each input example. While conceptually appealing, existing sparse gates, such as Top-k, are not smooth. The lack of smoothness can lead to convergence and statistical performance issues when training with gradient-based methods. In this paper, we develop DSelect-k: a continuously differentiable and sparse gate for MoE, based on a novel binary encoding formulation. The gate can be trained using first-order methods, such as stochastic gradient descent, and offers explicit control over the number of experts to select. We demonstrate the effectiveness of DSelect-k on both synthetic and real MTL datasets with up to 128 tasks. Our experiments indicate that DSelect-k can achieve statistically significant improvements in prediction and expert selection over popular MoE gates. Notably, on a real-world, large-scale recommender system, DSelect-k achieves over 22% improvement in predictive performance compared to Top-k. We provide an open-source implementation of DSelect-k.

Author Information

Hussein Hazimeh (Google)
Zhe Zhao (Google)
Aakanksha Chowdhery (Google)
Maheswaran Sathiamoorthy (Google AI)
Yihua Chen (Google)
Rahul Mazumder (MIT)
Lichan Hong (Google)
Ed Chi (Google Inc.)

d H. Chi is a Principal Scientist at Google, leading several machine learning research teams focusing on neural modeling, inclusive ML, reinforcement learning, and recommendation systems in Google Brain team. He has delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >325 product launches in the last 6 years. With 39 patents and over 120 research articles, he is also known for research on user behavior in web and social media. Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he recently received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.

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