Search Results for author:
Found 15 papers, 5 papers with code
Searching for Legal Documents at Paragraph Level: Automating Label Generation and Use of an Extended Attention Mask for Boosting Neural Models of Semantic Similarity
For this domain, we formulated a task to compare different ways of modeling semantic similarity at paragraph level, using neural and non-neural systems.
Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks
This mechanism leverages audio and visual modalities as soft prompts to dynamically adjust the parameters of pre-trained models based on the current multi-modal input features.
Gloss Attention for Gloss-free Sign Language Translation
We find that it can provide two aspects of information for the model, 1) it can help the model implicitly learn the location of semantic boundaries in continuous sign language videos, 2) it can help the model understand the sign language video globally.
Ranked #3 on
Gloss-free Sign Language Translation
on PHOENIX14T
Connecting Multi-modal Contrastive Representations
This paper proposes a novel training-efficient method for learning MCR without paired data called Connecting Multi-modal Contrastive Representations (C-MCR).
Enhanced multilayer perceptron with feature selection and grid search for travel mode choice prediction
Accurate and reliable prediction of individual travel mode choices is crucial for developing multi-mode urban transportation systems, conducting transportation planning and formulating traffic demand management strategies.
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
In recent years, deep learning brings improvements to place recognition by learnable feature extraction.
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to recommend the next item via leveraging the mixed user behaviors in multiple domains.
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains.
UZH at SemEval-2020 Task 3: Combining BERT with WordNet Sense Embeddings to Predict Graded Word Similarity Changes
CoSimLex is a dataset that can be used to evaluate the ability of context-dependent word embed- dings for modeling subtle, graded changes of meaning, as perceived by humans during reading.
Machine learning forecasts of the cosmic distance duality relation with strongly lensed gravitational wave events
We use simulated strongly lensed gravitational wave events from the Einstein Telescope to demonstrate how the luminosity and angular diameter distances, $d_L(z)$ and $d_A(z)$ respectively, can be combined to test in a model independent manner for deviations from the cosmic distance duality relation and the standard cosmological model.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology
Improving the generalization of network based relative pose regression: dimension reduction as a regularizer
Through experiments on real world RGBD datasets we validate the effectiveness of our design in terms of improving both generalization performance and robustness towards viewpoint change, and also show the potential of regression based visual localization networks towards challenging occasions that are difficult for geometry based visual localization methods.
Towards navigation without precise localization: Weakly supervised learning of goal-directed navigation cost map
Autonomous navigation based on precise localization has been widely developed in both academic research and practical applications.
Robotics
Erasure coding for distributed matrix multiplication for matrices with bounded entries
We demonstrate a tradeoff between the assumed absolute value bounds on the matrix entries and the recovery threshold.
LocNet: Global localization in 3D point clouds for mobile vehicles
Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge.
3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection
This paper aims at developing a faster and a more accurate solution to the amodal 3D object detection problem for indoor scenes.
