Glad to share my first preprint! We introduce a biologically-inspired parametric activation, and investigate neural adaptation in RNNs. arxiv.org/abs/2006.12253 Work done milamontreal, with Giancarlo Kerg, Stefan Horoi and Guy Wolf, and under the mentorship of Guillaume Lajoie.

Excited to announce our latest work (along with B.Kanuparthi, A. Goyal, K. Goyette, Y. Bengio and G. Lajoie): Untangling tradeoffs between recurrence and self-attention in neural networks arxiv.org/abs/2006.09471 Anirudh Goyal Guillaume Lajoie

Canada is home to a collaborative network of some of the most creative and brilliant thinkers in AI today. Google Canada recognizes the importance of investing in curiosity-driven research and we're thrilled to reconfirm our support of @Mila_Québec 👉 bit.ly/38GwPwW

Every symmetry of a network has a corresponding conserved quantity through training under gradient flow (Noether's theorem for neural networks!) For translation, scale, and rescale symmetry the flow is constrained to a hyperplane, sphere, and hyperbola respectively 4/8

#2020