Search Results for author:
Found 11 papers, 3 papers with code
Non-asymptotic Convergence of Discrete-time Diffusion Models: New Approach and Improved Rate
In this paper, we establish the convergence guarantee for substantially larger classes of distributions under discrete-time diffusion models and further improve the convergence rate for distributions with bounded support.
Energy Transformer
Our work combines aspects of three promising paradigms in machine learning, namely, attention mechanism, energy-based models, and associative memory.
Quickest Change Detection with Leave-one-out Density Estimation
The problem of quickest change detection in a sequence of independent observations is considered.
Associative Learning for Network Embedding
The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information.
Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings
Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document.
Quickest Change Detection with Non-Stationary Post-Change Observations
For the case where the post-change distributions have parametric uncertainty, a window-limited (WL) generalized likelihood-ratio (GLR) CuSum procedure is developed and is shown to achieve the universal lower bound asymptotically.
Non-Parametric Quickest Mean Change Detection
For the case where the pre-change distribution is known, a test is derived that asymptotically minimizes the worst-case detection delay over all possible post-change distributions, as the false alarm rate goes to zero.
Can a Fruit Fly Learn Word Embeddings?
In this work we study a mathematical formalization of this network motif and apply it to learning the correlational structure between words and their context in a corpus of unstructured text, a common natural language processing (NLP) task.
Non-Parametric Quickest Detection of a Change in the Mean of an Observation Sequence
We study the problem of quickest detection of a change in the mean of an observation sequence, under the assumption that both the pre- and post-change distributions have bounded support.
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
End-to-end sequential learning to explore spatialtemporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i. e., even the largest video segmentation dataset only contains 90 short video clips.
Ranked #12 on
Video Object Segmentation
on YouTube-VOS 2018
(F-Measure (Unseen) metric)
