Search
Now showing items 1-10 of 17
Deep compositional robotic planners that follow natural language commands
(Center for Brains, Minds and Machines (CBMM), Computation and Systems Neuroscience (Cosyne), 2020-05-31)
We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and manipu- late objects. Our approach combines ...
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
(Center for Brains, Minds and Machines (CBMM), The Ninth International Conference on Learning Representations (ICLR), 2020-10-25)
We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions. The input LTL formulas have never been seen before, ...
For interpolating kernel machines, the minimum norm ERM solution is the most stable
(Center for Brains, Minds and Machines (CBMM), 2020-06-22)
We study the average CVloo stability of kernel ridge-less regression and derive corresponding risk bounds. We show that the interpolating solution with minimum norm has the best CVloo stability, which in turn is controlled ...
Dreaming with ARC
(Center for Brains, Minds and Machines (CBMM), 2020-11-23)
Current machine learning algorithms are highly specialized to whatever it is they are meant to do –– e.g. playing chess, picking up objects, or object recognition. How can we extend this to a system that could solve a ...
Learning a natural-language to LTL executable semantic parser for grounded robotics
(Center for Brains, Minds and Machines (CBMM), Conference on Robot Learning (CoRL), 2020-11-16)
Children acquire their native language with apparent ease by observing how language is used in context and attempting to use it themselves. They do so without laborious annotations, negative examples, or even direct ...
Hierarchically Local Tasks and Deep Convolutional Networks
(Center for Brains, Minds and Machines (CBMM), 2020-06-24)
The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. ...
An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation
(Center for Brains, Minds and Machines (CBMM), 2020-04-15)
We propose an exit strategy from the COVID-19 lockdown, which is based on a risk-sensitive levels of social distancing. At the heart of our approach is the realization that the most effective, yet limited in number, resources ...
A Definition of General Problem Solving
(2020-07-13)
What is general intelligence? What does it mean by general problem solving? We attempt to give a definition of general problem solving, characterize the common process of problem solving and provide a basic algorithm that ...
Biologically Inspired Mechanisms for Adversarial Robustness
(Center for Brains, Minds and Machines (CBMM), 2020-06-23)
A convolutional neural network strongly robust to adversarial perturbations at reasonable computational and performance cost has not yet been demonstrated. The primate visual ventral stream seems to be robust to small ...
Stable Foundations for Learning: a foundational framework for learning theory in both the classical and modern regime.
(Center for Brains, Minds and Machines (CBMM), 2020-03-25)
We consider here the class of supervised learning algorithms known as Empirical Risk Minimization (ERM). The classical theory by Vapnik and others characterize universal consistency of ERM in the classical regime in which ...