Search Results for author:
Found 19 papers, 7 papers with code
InstInfer: In-Storage Attention Offloading for Cost-Effective Long-Context LLM Inference
Several cost-effective solutions leverage host memory or SSDs to reduce storage costs for offline inference scenarios and improve the throughput.
Multi-scale Contrastive Adaptor Learning for Segmenting Anything in Underperformed Scenes
In this paper, we introduce a novel Multi-scale Contrastive Adaptor learning method named MCA-SAM, which enhances adaptor performance through a meticulously designed contrastive learning framework at both token and sample levels.
Contrastive masked auto-encoders based self-supervised hashing for 2D image and 3D point cloud cross-modal retrieval
Simply applying existing cross-modal approaches to this new task fails to adequately capture latent multi-modal semantics and effectively bridge the modality gap between 2D and 3D.
NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers
AI regulations are expected to prohibit machine learning models from using sensitive attributes during training.
SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction
Quantitatively, we evaluate our method across three publicly available medical imaging datasets, and find that our approach leads to up to a 15. 5\% improvement in contour segmentation accuracy compared to zero-shot SAM.
End-to-End Autoregressive Retrieval via Bootstrapping for Smart Reply Systems
Reply suggestion systems represent a staple component of many instant messaging and email systems.
CHAIN: Exploring Global-Local Spatio-Temporal Information for Improved Self-Supervised Video Hashing
Compressing videos into binary codes can improve retrieval speed and reduce storage overhead.
Contextual Knowledge Learning For Dialogue Generation
We guide the model training through a Contextual Knowledge Learning (CKL) process which involves Latent Vectors for context and knowledge, respectively.
Model-Based Simulation for Optimising Smart Reply
To perform well at this task, a system should be able to effectively present the user with a diverse set of options, to maximise the chance that at least one of them conveys the user's desired response.
Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games
Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP.
Mean-variance hybrid portfolio optimization with quantile-based risk measure
This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation.
Hyperbolic Hierarchical Contrastive Hashing
To exploit the hierarchical semantic structures in hyperbolic space, we designed the hierarchical contrastive learning algorithm, including hierarchical instance-wise and hierarchical prototype-wise contrastive learning.
Foreground Object Structure Transfer for Unsupervised Domain Adaptation
In practice, since the label of the target domain is not available, we use the clustering information of the source domain to assign pseudo labels to the target domain samples, and then according to the source domain data prior knowledge guides those positive features to maximum the inter-class distance between different classes and mimimum the intra-class distance.
POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling
Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems.
What and How long: Prediction of Mobile App Engagement
Based on the analysis, we further investigate a novel mobile app engagement prediction problem -- can we predict simultaneously what app the user will use next and how long he/she will stay on that app?
Meta-evaluation of Conversational Search Evaluation Metrics
Conversational search systems, such as Google Assistant and Microsoft Cortana, enable users to interact with search systems in multiple rounds through natural language dialogues.
The Healthy States of America: Creating a Health Taxonomy with Social Media
We validated the structure of our taxonomy against the official International Statistical Classification of Diseases and Related Health Problems (ICD-11), finding matches of our clusters with 20 official categories, out of 22.
Approximation of Response Knowledge Retrieval in Knowledge-grounded Dialogue Generation
This paper is concerned with improving dialogue generation models through injection of knowledge, e. g., content relevant to the post that can increase the quality of responses.
