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Found 28 papers, 9 papers with code
Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task
This paper describes the Tencent AI Lab’s submission of the WMT 2020 shared task on chat translation in English-German.
DALex: Lexicase-like Selection via Diverse Aggregation
Lexicase selection has been shown to provide advantages over other selection algorithms in several areas of evolutionary computation and machine learning.
Optimizing Neural Networks with Gradient Lexicase Selection
Lexicase selection is an uncompromising method developed in evolutionary computation, which selects models on the basis of sequences of individual training case errors instead of using aggregated metrics such as loss and accuracy.
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance
Furthermore, we find that this technique results in competitive performance on the diversity-focused metrics of QD-Score and Coverage, without explicitly optimizing for these things.
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol.
Quality Diversity through Human Feedback
Meanwhile, Quality Diversity (QD) algorithms excel at identifying diverse and high-quality solutions but often rely on manually crafted diversity metrics.
CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild
Non-intrusive, real-time analysis of the dynamics of the eye region allows us to monitor humans' visual attention allocation and estimate their mental state during the performance of real-world tasks, which can potentially benefit a wide range of human-computer interaction (HCI) applications.
Particularity
We describe a design principle for adaptive systems under which adaptation is driven by particular challenges that the environment poses, as opposed to average or otherwise aggregated measures of performance over many challenges.
Probabilistic Lexicase Selection
Lexicase selection is a widely used parent selection algorithm in genetic programming, known for its success in various task domains such as program synthesis, symbolic regression, and machine learning.
Efficient Halftoning via Deep Reinforcement Learning
In this paper, we propose a fast and structure-aware halftoning method via a data-driven approach.
DeepMapping2: Self-Supervised Large-Scale LiDAR Map Optimization
LiDAR mapping is important yet challenging in self-driving and mobile robotics.
Lexicase Selection at Scale
Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream.
Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
Recent works show that parameterized quantum circuits (PQCs) can be used to solve challenging reinforcement learning (RL) tasks with provable learning advantages.
Self-Supervised Visual Place Recognition by Mining Temporal and Feature Neighborhoods
Inspired by noisy label learning, we propose a novel self-supervised framework named \textit{TF-VPR} that uses temporal neighborhoods and learnable feature neighborhoods to discover unknown spatial neighborhoods.
Halftoning with Multi-Agent Deep Reinforcement Learning
Deep neural networks have recently succeeded in digital halftoning using vanilla convolutional layers with high parallelism.
Evolving Neural Selection with Adaptive Regularization
We propose the Adaptive Neural Selection (ANS) framework, which evolves to weigh neurons in a layer to form network variants that are suitable to handle different input cases.
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework
Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images.
Deep Weakly Supervised Positioning
PoseNet can map a photo to the position where it is taken, which is appealing in robotics.
Perception Matters: Exploring Imperceptible and Transferable Anti-forensics for GAN-generated Fake Face Imagery Detection
Specially, when adversaries consider imperceptibility as a constraint, the proposed anti-forensic method can improve the average attack success rate by around 30\% on fake face images over two baseline attacks.
Object as Distribution
We provide qualitative evaluation of this representation for the object detection task and quantitative evaluation of its use in a baseline algorithm for the instance segmentation task.
A Novel Deep Learning Pipeline for Retinal Vessel Detection in Fluorescein Angiography
We propose a novel pipeline to detect retinal vessels in FA images using deep neural networks that reduces the effort required for generating labeled ground truth data by combining two key components: cross-modality transfer and human-in-the-loop learning.
Value of Temporal Dynamics Information in Driving Scene Segmentation
What is not known is how much extra information the temporal dynamics of the visual scene carries that is complimentary to the information available in the individual frames of the video.
DeepMapping: Unsupervised Map Estimation From Multiple Point Clouds
We propose DeepMapping, a novel registration framework using deep neural networks (DNNs) as auxiliary functions to align multiple point clouds from scratch to a globally consistent frame.
Weakly-Supervised Action Segmentation with Iterative Soft Boundary Assignment
In this work, we address the task of weakly-supervised human action segmentation in long, untrimmed videos.
Video Action Segmentation with Hybrid Temporal Networks
Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years.
MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation
For the foreseeble future, human beings will likely remain an integral part of the driving task, monitoring the AI system as it performs anywhere from just over 0% to just under 100% of the driving.
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
We consider the paradigm of a black box AI system that makes life-critical decisions.
TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation
Action segmentation as a milestone towards building automatic systems to understand untrimmed videos has received considerable attention in the recent years.
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