IMP-MoE-L focusing on video tasks that achieves new state-of-the-art in zero-shot video classification: 77.0% on Kinetics-400, 76.8% on Kinetics-600, and 68.3% on

Most widely adopted MoE models are discontinuous with respect to their parameters - often referred to as sparse .

Implicit neural representations (INRs) have proven effective in various tasks including image, shape, audio, and video reconstruction. These INRs typically learn the implicit field from sampled input points.

MTL: InMTL, deep learning-based architectures that perform soft-parameter sharing, i.e., share model parameters partially, are proving to be effective at exploiting both the commonalities and differences among tasks [6]. Ourwork is also related to [5] who introduced "routers" (similar to gates) that can choose which layers or components of layers to activate per-task. The routers in the latter work are not differentiable and requirereinforcementlearning. To construct α, there are two cases to consider: (i)s = k and (ii) s < k. If s = k, then set αi = log(w ti) for i [k]. Our base case is fort = 1.

This motivates us to consider a challenging classification problem with intrinsic cluster structures.