Search results

Deep Metric Learning (DML) methods aim at learning an embedding space in which distances are closely related to the inherent semantic similarity of the inputs. Previous studies have shown that popular benchmark datasets often contain numerous wrong labels, and DML methods are susceptible to them. Intending to study the effect of realistic noise, we create an ontology of the classes in a dataset and use

Semi-supervised semantic segmentation methods use a small amount of clean pixel-level annotations to guide the interpretation of a larger quantity of unlabelled image data. The challenges of providing pixel-accurate annotations at scale mean that the labels are typically noisy, and this contaminates the final results. In this work, we propose an approach that is robust to label noise in the annotated data

Adopting AI in financial advisory is a challenging task as there exists multiple sources of information to digest and interpret. Such information consumption process are very lengthy for financial advisors, reducing the efficiency and timeliness for their advice and recommendation given to their clients. In this work, we introduce a multi-step framework that consumes and combines news and industry-focused

Vision-language models have been widely explored across a wide range of tasks and achieve satisfactory performance. However, it’s under-explored how to consolidate entity understanding through a varying number of images and to align it with the pre-trained language models for generative tasks. In this paper, we propose MIVC, a general multiple instance visual component to bridge the gap between various

Current LLM summarization systems Produce broad overviews which are disconnected from people specific interests and expectations. Basically, people preferences (topics) can be expressed by a collection of semantic keywords. Previous work exploit these keywords as extra input to generate summary. That requires additional human annotations. To tackle these constraints, we propose a novel framework, Topic

Grounding-based vision and language models have been successfully applied to low-level vision tasks, aiming to pre- cisely locate objects referred in captions. The effectiveness of grounding representation learning heavily relies on the scale of the training dataset. Despite being a useful data en- richment strategy, data augmentation has received minimal attention in existing vision and language tasks

Deep neural networks (DNNs) have improved NLP tasks significantly, but training and maintaining such networks could be costly. Model compression techniques, such as, knowledge distillation (KD), have been proposed to address the issue; however, the compression process could be lossy. Motivated by this, our work investigates how a distilled student model differs from its teacher, if the distillation process

Personalization, the ability to tailor a system to individual users, is an essential factor in user experience with natural language process- ing (NLP) systems. With the emergence of Large Language Models (LLMs), a key question is how to leverage these models to better personalize user experiences. To personalize a language model’s output, a straightforward approach is to incorporate past user data into

Moving toward a full suite of proof-producing automated reasoning tools with SMT solvers that can produce full, independently checkable proofs for real-world problems.

Applied Scientist

Applied Scientist

Data Scientist

Data Scientist

Applied Scientist

Data Scientist

Research Scientist

Data Scientist