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David Ding

3 accepted papers

2022

Perceiver IO: A General Architecture for Structured Inputs & Outputs

ICLR 2022spotlight

A central goal of machine learning is the development of systems that can solve many problems in as many data domains as possible. Current architectures, however, cannot be applied beyond a small set of stereotyped settings, as they bake in domain & task assumptions or scale poorly to large inputs o…

2021

Attention over Learned Object Embeddings Enables Complex Visual Reasoning

NeurIPS 2021oral

Neural networks have achieved success in a wide array of perceptual tasks but often fail at tasks involving both perception and higher-level reasoning. On these more challenging tasks, bespoke approaches (such as modular symbolic components, independent dynamics models or semantic parsers) targeted…

2017

Deep Lattice Networks and Partial Monotonic Functions

NeurIPS 2017poster

We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network…