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Found 25 papers, 8 papers with code
Sequence-to-sequence AMR Parsing with Ancestor Information
AMR parsing is the task that maps a sentence to an AMR semantic graph automatically.
Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing
To equip the graph processing with both high accuracy and explainability, we introduce a novel approach that harnesses the power of a large language model (LLM), enhanced by an uncertainty-aware module to provide a confidence score on the generated answer.
TSViT: A Time Series Vision Transformer for Fault Diagnosis
Traditional fault diagnosis methods using Convolutional Neural Networks (CNNs) face limitations in capturing temporal features (i. e., the variation of vibration signals over time).
Translating Natural Language to Planning Goals with Large-Language Models
Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their applicability across various domains.
Sim-to-Real Transfer for Quadrupedal Locomotion via Terrain Transformer
Deep reinforcement learning has recently emerged as an appealing alternative for legged locomotion over multiple terrains by training a policy in physical simulation and then transferring it to the real world (i. e., sim-to-real transfer).
Strictly Breadth-First AMR Parsing
AMR parsing is the task that maps a sentence to an AMR semantic graph automatically.
Compact and Robust Deep Learning Architecture for Fluorescence Lifetime Imaging and FPGA Implementation
FLAN+LS on hardware achieves the highest computing efficiency compared to 1-D CNN and FLAN.
Action Recognition based on Cross-Situational Action-object Statistics
We set out to identify properties of training data that lead to action recognition models with greater generalization ability.
Clairvoyance: Intelligent Route Planning for Electric Buses Based on Urban Big Data
Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce local carbon emissions.
ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition
We present the visually-grounded language modelling track that was introduced in the Zero-Resource Speech challenge, 2021 edition, 2nd round.
Feature importance recap and stacking models for forex price prediction
Traditionally, traders refer to technical analysis based on the historical data to make decisions and trade.
Reverse-engineer the Distributional Structure of Infant Egocentric Views for Training Generalizable Image Classifiers
We analyze egocentric views of attended objects from infants.
Sparse online relative similarity learning
This is clearly inefficient for high dimensional tasks due to its high memory and computational complexity.
A Computational Model of Early Word Learning from the Infant's Point of View
Human infants have the remarkable ability to learn the associations between object names and visual objects from inherently ambiguous experiences.
A Self Validation Network for Object-Level Human Attention Estimation
Due to the foveated nature of the human vision system, people can focus their visual attention on a small region of their visual field at a time, which usually contains only a single object.
Active Object Manipulation Facilitates Visual Object Learning: An Egocentric Vision Study
Inspired by the remarkable ability of the infant visual learning system, a recent study collected first-person images from children to analyze the `training data' that they receive.
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression
For example, under the popular parameter server model for distributed learning, the worker nodes need to send the compressed local gradients to the parameter server, which performs the aggregation.
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss.
Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend
Decentralized Online Learning (online learning in decentralized networks) attracts more and more attention, since it is believed that Decentralized Online Learning can help the data providers cooperatively better solve their online problems without sharing their private data to a third party or other providers.
Toddler-Inspired Visual Object Learning
Real-world learning systems have practical limitations on the quality and quantity of the training datasets that they can collect and consider.
Distributed Learning over Unreliable Networks
Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e. g., gradients or models), the network should guarantee the delivery of the message.
AutoML from Service Provider's Perspective: Multi-device, Multi-tenant Model Selection with GP-EI
In this paper, we focus on the AutoML problem from the \emph{service provider's perspective}, motivated by the following practical consideration: When an AutoML service needs to serve {\em multiple users} with {\em multiple devices} at the same time, how can we allocate these devices to users in an efficient way?
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.
Lending A Hand: Detecting Hands and Recognizing Activities in Complex Egocentric Interactions
Hands appear very often in egocentric video, and their appearance and pose give important cues about what people are doing and what they are paying attention to.
Human mobility synthesis using matrix and tensor factorizations
As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data.
