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Found 14 papers, 9 papers with code
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts
A key component of modern conversational systems is the Dialogue State Tracker (or DST), which models a user's goals and needs.
Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions
Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction.
Evolution of Popularity Bias: Empirical Study and Debiasing
and how to debias in this long-term dynamic process?
Density-preserving Deep Point Cloud Compression
To address this, we propose a novel deep point cloud compression method that preserves local density information.
MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Tasks
Specifically, in each training iteration and adaptively for each part of the network, the gradient of an auxiliary loss is carefully reduced or enlarged to have a closer magnitude to the gradient of the target loss, preventing auxiliary tasks from being so strong that dominate the target task or too weak to help the target task.
HyperPrompt: Prompt-based Task-Conditioning of Transformers
Prompt-Tuning is a new paradigm for finetuning pre-trained language models in a parameter-efficient way.
Popularity-Opportunity Bias in Collaborative Filtering
This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias.
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge
We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition
Knowledge of a disease includes information of various aspects of the disease, such as signs and symptoms, diagnosis and treatment.
Consistency-Aware Recommendation for User-Generated ItemList Continuation
Complementary to methods that exploit specific content patterns (e. g., as in song-based playlists that rely on audio features), the proposed approach models the consistency of item lists based on human curation patterns, and so can be deployed across a wide range of varying item types (e. g., videos, images, books).
A Hierarchical Self-Attentive Model for Recommending User-Generated Item Lists
User-generated item lists are a popular feature of many different platforms.
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper. challenge.
Motion Representation with Acceleration Images
Information of time differentiation is extremely important cue for a motion representation.
Human Action Recognition without Human
The objective of this paper is to evaluate "human action recognition without human".
