Search Results for author:
Found 9 papers, 5 papers with code
Slot Abstractors: Toward Scalable Abstract Visual Reasoning
Abstract visual reasoning is a characteristically human ability, allowing the identification of relational patterns that are abstracted away from object features, and the systematic generalization of those patterns to unseen problems.
A Prefrontal Cortex-inspired Architecture for Planning in Large Language Models
To address this, we take inspiration from the human brain, in which planning is accomplished via the recurrent interaction of specialized modules in the prefrontal cortex (PFC).
Determinantal Point Process Attention Over Grid Cell Code Supports Out of Distribution Generalization
Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies.
Learning to reason over visual objects
These results suggest that an inductive bias for object-centric processing may be a key component of abstract visual reasoning, obviating the need for problem-specific inductive biases.
Generalization to Out-of-Distribution transformations
Humans understand a set of canonical geometric transformations (such as translation, rotation and scaling) that support generalization by being untethered to any specific object.
DeepPlace: Learning to Place Applications in Multi-Tenant Clusters
Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics.
Investment Ranking Challenge: Identifying the best performing stocks based on their semi-annual returns
In the IEEE Investment ranking challenge 2018, participants were asked to build a model which would identify the best performing stocks based on their returns over a forward six months window.
KarNet: An Efficient Boolean Function Simplifier
Many approaches such as Quine-McCluskey algorithm, Karnaugh map solving, Petrick's method and McBoole's method have been devised to simplify Boolean expressions in order to optimize hardware implementation of digital circuits.
Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos
Surgical workflow analysis is of importance for understanding onset and persistence of surgical phases and individual tool usage across surgery and in each phase.
