Search Results for author:
Found 27 papers, 4 papers with code
Explaining Autonomous Driving Actions with Visual Question Answering
The end-to-end learning ability of self-driving vehicles has achieved significant milestones over the last decade owing to rapid advances in deep learning and computer vision algorithms.
Reparameterization through Spatial Gradient Scaling
Reparameterization aims to improve the generalization of deep neural networks by transforming convolutional layers into equivalent multi-branched structures during training.
A General-Purpose Transferable Predictor for Neural Architecture Search
In this paper, we propose a general-purpose neural predictor for NAS that can transfer across search spaces, by representing any given candidate Convolutional Neural Network (CNN) with a Computation Graph (CG) that consists of primitive operators.
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble.
AIO-P: Expanding Neural Performance Predictors Beyond Image Classification
Evaluating neural network performance is critical to deep neural network design but a costly procedure.
Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
First, we provide a thorough overview of the state-of-the-art studies on XAI for autonomous driving.
Towards Safe, Explainable, and Regulated Autonomous Driving
There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning.
Autonomous Driving
Explainable Artificial Intelligence (XAI)
+1
A Transferable General-Purpose Predictor for Neural Architecture Search
Understanding and modelling the performance of neural architectures is key to Neural Architecture Search (NAS).
L$^{2}$NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning
In this paper, we propose L$^{2}$NAS, which learns to intelligently optimize and update architecture hyperparameters via an actor neural network based on the distribution of high-performing architectures in the search history.
Generative Adversarial Neural Architecture Search
Despite the empirical success of neural architecture search (NAS) in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to assess.
Generative Adversarial Neural Architecture Search with Importance Sampling
Despite the empirical success of neural architecture search (NAS) algorithms in deep learning applications, the optimality, reproducibility and cost of NAS schemes remain hard to be assessed.
Neural Architecture Search For Keyword Spotting
Deep neural networks have recently become a popular solution to keyword spotting systems, which enable the control of smart devices via voice.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V1 6 metric)
ADIDA: Automatic Dialect Identification for Arabic
This demo paper describes ADIDA, a web-based system for automatic dialect identification for Arabic text.
Fine-Grained Arabic Dialect Identification
Previous work on the problem of Arabic Dialect Identification typically targeted coarse-grained five dialect classes plus Standard Arabic (6-way classification).
SemEval-2018 Task 1: Affect in Tweets
We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet.
Sentiment Lexicons for Arabic Social Media
Existing Arabic sentiment lexicons have low coverage―with only a few thousand entries.
