Search Results for author:
Found 18 papers, 3 papers with code
iLab at FinCausal 2022: Enhancing Causality Detection with an External Cause-Effect Knowledge Graph
The application of span detection grows fast along with the increasing need of understanding the causes and effects of events, especially in the finance domain.
Reasoning LLMs are Wandering Solution Explorers
Large Language Models (LLMs) have demonstrated impressive reasoning abilities through test-time computation (TTC) techniques such as chain-of-thought prompting and tree-based reasoning.
Bullying the Machine: How Personas Increase LLM Vulnerability
Large Language Models (LLMs) are increasingly deployed in interactions where they are prompted to adopt personas.
Technical Report for ICML 2024 TiFA Workshop MLLM Attack Challenge: Suffix Injection and Projected Gradient Descent Can Easily Fool An MLLM
This technical report introduces our top-ranked solution that employs two approaches, \ie suffix injection and projected gradient descent (PGD) , to address the TiFA workshop MLLM attack challenge.
Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation
Instrumental Variable (IV) provides a source of treatment randomization that is conditionally independent of the outcomes, responding to the challenges of counterfactual and confounding biases.
Strong Preferences Affect the Robustness of Value Alignment
A key component of value alignment is the modeling of human preferences as a representation of human values.
Rehearsing Answers to Probable Questions with Perspective-Taking
Question answering (QA) has been a long-standing focus in the NLP field, predominantly addressing reading comprehension and common sense QA.
Bridging the Intent Gap: Knowledge-Enhanced Visual Generation
To address these challenges, we propose a knowledge-enhanced iterative refinement framework for visual content generation.
Hallucination is Inevitable: An Innate Limitation of Large Language Models
Since the formal world is a part of the real world which is much more complicated, hallucinations are also inevitable for real world LLMs.
Bilateral Adaptation for Human-Object Interaction Detection with Occlusion-Robustness
Human-Object Interaction (HOI) Detection constitutes an important aspect of human-centric scene understanding which requires precise object detection and interaction recognition.
A Study on Differentiable Logic and LLMs for EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2023
Our research focuses on the innovative application of a differentiable logic loss in the training to leverage the co-occurrence relations between verb and noun, as well as the pre-trained Large Language Models (LLMs) to generate the logic rules for the adaptation to unseen action labels.
DA$^2$ Dataset: Toward Dexterity-Aware Dual-Arm Grasping
In this paper, we introduce DA$^2$, the first large-scale dual-arm dexterity-aware dataset for the generation of optimal bimanual grasping pairs for arbitrary large objects.
Unsupervised Motion Representation Learning with Capsule Autoencoders
We propose the Motion Capsule Autoencoder (MCAE), which addresses a key challenge in the unsupervised learning of motion representations: transformation invariance.
Representation Learning
Self-Supervised Human Action Recognition
+2
Relation-aware Compositional Zero-shot Learning for Attribute-Object Pair Recognition
The concept module generates semantically meaningful features for primitive concepts, whereas the visual module extracts visual features for attributes and objects from input images.
GradMix: Multi-source Transfer across Domains and Tasks
Therefore, it is important to develop algorithms that can leverage off-the-shelf labeled dataset to learn useful knowledge for the target task.
Embedding Symbolic Knowledge into Deep Networks
Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding.
Comparing of Term Clustering Frameworks for Modular Ontology Learning
The construction of this co-occurrence matrix from context helps to build feature space of noun phrases, which is then transformed to several encoding representations including feature selection and dimensionality reduction.
Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation
Towards robust and convenient indoor shopping mall navigation, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users.
