Search Results for author:
Found 15 papers, 6 papers with code
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning
Reinforcement learning agents need exploratory behaviors to escape from local optima.
SNAS: Stochastic Neural Architecture Search
In experiments on CIFAR-10, SNAS takes less epochs to find a cell architecture with state-of-the-art accuracy than non-differentiable evolution-based and reinforcement-learning-based NAS, which is also transferable to ImageNet.
Ranked #25 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-10
Graph-guided Architecture Search for Real-time Semantic Segmentation
Unlike previous works that use a simplified search space and stack a repeatable cell to form a network, we introduce a novel search mechanism with new search space where a lightweight model can be effectively explored through the cell-level diversity and latencyoriented constraint.
Learning a Decision Module by Imitating Driver's Control Behaviors
In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning.
DSNAS: Direct Neural Architecture Search without Parameter Retraining
We argue that given a computer vision task for which a NAS method is expected, this definition can reduce the vaguely-defined NAS evaluation to i) accuracy of this task and ii) the total computation consumed to finally obtain a model with satisfying accuracy.
Ranked #16 on
Neural Architecture Search
on NAS-Bench-201, ImageNet-16-120
(Accuracy (Val) metric)
Understanding the wiring evolution in differentiable neural architecture search
To this end, we pose questions that future differentiable methods for neural wiring discovery need to confront, hoping to evoke a discussion and rethinking on how much bias has been enforced implicitly in existing NAS methods.
Generalized Inverse Planning: Learning Lifted non-Markovian Utility for Generalizable Task Representation
Seeing that the proposed generalization problem has not been widely studied yet, we carefully define an evaluation protocol, with which we illustrate the effectiveness of MEIP on two proof-of-concept domains and one challenging task: learning to fold from demonstrations.
Learning Algebraic Representation for Abstract Spatial-Temporal Reasoning
We further show that the algebraic representation learned can be decoded by isomorphism and used to generate an answer.
HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving
Leveraging these concepts, they could understand the internal structure of this task, without seeing all of the problem instances.
Unsupervised Foreground Extraction via Deep Region Competition
Foreground extraction can be viewed as a special case of generic image segmentation that focuses on identifying and disentangling objects from the background.
Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning
Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization.
Emergent Graphical Conventions in a Visual Communication Game
Humans communicate with graphical sketches apart from symbolic languages.
Latent Diffusion Energy-Based Model for Interpretable Text Modeling
Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling.
ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation
We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation.
Latent Plan Transformer: Planning as Latent Variable Inference
We introduce the Latent Plan Transformer (LPT), a novel model that leverages a latent space to connect a Transformer-based trajectory generator and the final return.
