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Found 7 papers, 3 papers with code
How Accurately Do Large Language Models Understand Code?
A key factor in these tasks' success is the model's deep understanding of code.
DMC-VB: A Benchmark for Representation Learning for Control with Visual Distractors
In this paper, we present theDeepMind Control Visual Benchmark (DMC-VB), a dataset collected in the DeepMind Control Suite to evaluate the robustness of offline RL agents for solving continuous control tasks from visual input in the presence of visual distractors.
Low-shot Object Learning with Mutual Exclusivity Bias
This paper introduces Low-shot Object Learning with Mutual Exclusivity Bias (LSME), the first computational framing of mutual exclusivity bias, a phenomenon commonly observed in infants during word learning.
Iterative Machine Teaching
Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner.
The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals
We propose a new energy minimization framework incorporating geodesic distances between segments which solves this problem.
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
Our proposed framework provides an efficient approach for finding temporally consistent occlusion boundaries in video by utilizing causality, redundancy in videos, and semantic layout of the scene.
RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions
By precomputing a graph which can be used for parametric min-cuts over different seeds, we speed up the generation of the segment pool.
