Search Results for author:
Found 26 papers, 9 papers with code
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor.
Scalable and Effective Generative Information Retrieval
This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks.
WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection
Quick detection of blade ice accretion is crucial for the maintenance of wind farms.
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs
Detecting anomalies in dynamic graphs is a vital task, with numerous practical applications in areas such as security, finance, and social media.
Knowledge Graph Reasoning over Entities and Numerical Values
To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures.
CAPS: A Practical Partition Index for Filtered Similarity Search
With the surging popularity of approximate near-neighbor search (ANNS), driven by advances in neural representation learning, the ability to serve queries accompanied by a set of constraints has become an area of intense interest.
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs.
SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series
However, branch and bound based pruning are only useful for very short queries (low dimensional time series), and the bounds are quite weak for longer queries.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Scaling-up Split-Merge MCMC with Locality Sensitive Sampling (LSS)
It is well known that state-of-the-art methods for split-merge MCMC do not scale well.
Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision
In this paper, the proposed framework takes a remarkably different direction to resolve the multi-scene detection problem in a bottom-up fashion.
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer
Given a well-trained dependency graph from a source domain and an immature dependency graph from a target domain, how can we extract the entity and dependency knowledge from the source to enhance the target?
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups
In the big-data world existing methods fail to address the new set of memory and latency constraints.
Unifying data for fine-grained visual species classification
Wildlife monitoring is crucial to nature conservation and has been done by manual observations from motion-triggered camera traps deployed in the field.
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches
Local patch matching is to find similar patches in a large neighborhood which can alleviate noise effect, but the number of patches may be insufficient.
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.
A Fast Randomized Algorithm for Massive Text Normalization
In addition, FLAN does not require any annotated data or supervised learning.
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
User sessions empower many search and recommendation tasks on a daily basis.
Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding
When applying masking to short search queries, most contextual information is lost and the intent of the search queries may be changed.
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
While previous studies have demonstrated the effectiveness of using user behavior signals (e. g., clicks) as both features and labels of LTR algorithms, we argue that existing LTR algorithms that indiscriminately treat behavior and non-behavior signals in input features could lead to suboptimal performance in practice.
CARAMEL: A Succinct Read-Only Lookup Table via Compressed Static Functions
Lookup tables are a fundamental structure in many data processing and systems applications.
Convex Latent-Optimized Adversarial Regularizers for Imaging Inverse Problems
We utilize it as a convex regularizer to formulate a CLEAR-informed variational regularization model that guides the solution of the imaging inverse problem on the real data manifold.
Matrix Completion-Informed Deep Unfolded Equilibrium Models for Self-Supervised k-Space Interpolation in MRI
However, most of these methods are tailored for supervised learning scenarios that necessitate fully sampled labels, which can pose challenges in practical MRI applications.
Joint Diffusion: Mutual Consistency-Driven Diffusion Model for PET-MRI Co-Reconstruction
The complementary information can contribute to image reconstruction.
Understanding Inter-Session Intentions via Complex Logical Reasoning
In this paper, we introduce the task of logical session complex query answering, where sessions are treated as hyperedges of items, and we formulate the problem of complex intention understanding as a task of logical session complex queries answering (LS-CQA) on an aggregated hypergraph of sessions, items, and attributes.
IterAlign: Iterative Constitutional Alignment of Large Language Models
Such a constitution discovery pipeline can be run iteratively and automatically to discover new constitutions that specifically target the alignment gaps in the current LLM.
