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Found 15 papers, 1 papers with code
Can Large Language Models Understand Context?
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.
MARRS: Multimodal Reference Resolution System
Successfully handling context is essential for any dialog understanding task.
Ontology Revision based on Pre-trained Language Models
We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones.
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem.
An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies
A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology.
Towards Modern Card Games with Large-Scale Action Spaces Through Action Representation
Axie infinity is a complicated card game with a huge-scale action space.
Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation
The UDA for ordinal classification requires inducing non-trivial ordinal distribution prior to the latent space.
Adversarial Unsupervised Domain Adaptation with Conditional and Label Shift: Infer, Align and Iterate
In this work, we propose an adversarial unsupervised domain adaptation (UDA) approach with the inherent conditional and label shifts, in which we aim to align the distributions w. r. t.
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization
The ground distance matrix can be pre-defined following a priori of hierarchical semantic risk.
MBB: Model-Based Baseline for Global Guidance of Model-Free Reinforcement Learning via Lower-Dimensional Solutions
On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.
Importance-Aware Semantic Segmentation in Self-Driving with Discrete Wasserstein Training
However, the cross entropy loss can not take the different importance of each class in an self-driving system into account.
Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving
The ground metric of Wasserstein distance can be pre-defined following the experience on a specific task.
AUTO3D: Novel view synthesis through unsupervisely learned variational viewpoint and global 3D representation
This paper targets on learning-based novel view synthesis from a single or limited 2D images without the pose supervision.
Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models
We test the verifier network on out-of-distribution detection and adversarial example detection problems, as well as anomaly detection problems in structured prediction tasks such as image caption generation.
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition
We consider the problem of comparing the similarity of image sets with variable-quantity, quality and un-ordered heterogeneous images.
