Search Results for author:
Found 15 papers, 8 papers with code
Merging Generated and Retrieved Knowledge for Open-Domain QA
Open-domain question answering (QA) systems are often built with retrieval modules.
Investigating Zero- and Few-shot Generalization in Fact Verification
In this paper, we explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e. g., Wikipedia) to low-resourced domains that lack human annotations.
Toward Optimized VR/AR Ergonomics: Modeling and Predicting User Neck Muscle Contraction
Leveraging electromyography devices, we measure, model, and predict VR users' neck muscle contraction levels (MCL) while they move their heads to interact with the virtual environment.
SituatedGen: Incorporating Geographical and Temporal Contexts into Generative Commonsense Reasoning
Generative commonsense reasoning is the task that requires machines, given a group of keywords, to compose a single coherent sentence with commonsense plausibility.
Force-Aware Interface via Electromyography for Natural VR/AR Interaction
Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users.
CC-Riddle: A Question Answering Dataset of Chinese Character Riddles
Solving Chinese character riddles is a challenging task that demands understanding of character glyph, general knowledge, and a grasp of figurative language.
Two-step Lookahead Bayesian Optimization with Inequality Constraints
Recent advances in computationally efficient non-myopic Bayesian optimization (BO) improve query efficiency over traditional myopic methods like expected improvement while only modestly increasing computational cost.
Constrained Two-step Look-Ahead Bayesian Optimization
Recent advances in computationally efficient non-myopic Bayesian optimization offer improved query efficiency over traditional myopic methods like expected improvement, with only a modest increase in computational cost.
Interpreting the Robustness of Neural NLP Models to Textual Perturbations
In this work, we test the hypothesis that the extent to which a model is affected by an unseen textual perturbation (robustness) can be explained by the learnability of the perturbation (defined as how well the model learns to identify the perturbation with a small amount of evidence).
BiRdQA: A Bilingual Dataset for Question Answering on Tricky Riddles
A riddle is a question or statement with double or veiled meanings, followed by an unexpected answer.
MOVER: Mask, Over-generate and Rank for Hyperbole Generation
In this paper, we tackle the challenging task of hyperbole generation to transfer a literal sentence into its hyperbolic paraphrase.
CaPC Learning: Confidential and Private Collaborative Learning
There is currently no method that enables machine learning in such a setting, where both confidentiality and privacy need to be preserved, to prevent both explicit and implicit sharing of data.
Demystify Painting with RL
Given an image of an arbitrary scene, experienced artists are skillful at accurately perceiving the visual contents within the scene, such as objects, lighting, and tint, and presenting them in different painting styles.
Exploiting Channel Similarity for Accelerating Deep Convolutional Neural Networks
To address the limitations of existing magnitude-based pruning algorithms in cases where model weights or activations are of large and similar magnitude, we propose a novel perspective to discover parameter redundancy among channels and accelerate deep CNNs via channel pruning.
Max-plus Operators Applied to Filter Selection and Model Pruning in Neural Networks
Following recent advances in morphological neural networks, we propose to study in more depth how Max-plus operators can be exploited to define morphological units and how they behave when incorporated in layers of conventional neural networks.
