5 research outputs found
COEM: Cross-Modal Embedding for MetaCell Identification
Metacells are disjoint and homogeneous groups of single-cell profiles,
representing discrete and highly granular cell states. Existing metacell
algorithms tend to use only one modality to infer metacells, even though
single-cell multi-omics datasets profile multiple molecular modalities within
the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal
\textbf{E}mbedding for \textbf{M}etaCell Identification (COEM), which utilizes
an embedded space leveraging the information of both scATAC-seq and scRNA-seq
to perform aggregation, balancing the trade-off between fine resolution and
sufficient sequencing coverage. COEM outperforms the state-of-the-art method
SEACells by efficiently identifying accurate and well-separated metacells
across datasets with continuous and discrete cell types. Furthermore, COEM
significantly improves peak-to-gene association analyses, and facilitates
complex gene regulatory inference tasks.Comment: 5 pages, 2 figures, ICML workshop on computational biolog
Sampling Through the Lens of Sequential Decision Making
Sampling is ubiquitous in machine learning methodologies. Due to the growth
of large datasets and model complexity, we want to learn and adapt the sampling
process while training a representation. Towards achieving this grand goal, a
variety of sampling techniques have been proposed. However, most of them either
use a fixed sampling scheme or adjust the sampling scheme based on simple
heuristics. They cannot choose the best sample for model training in different
stages. Inspired by "Think, Fast and Slow" (System 1 and System 2) in cognitive
science, we propose a reward-guided sampling strategy called Adaptive Sample
with Reward (ASR) to tackle this challenge. To the best of our knowledge, this
is the first work utilizing reinforcement learning (RL) to address the sampling
problem in representation learning. Our approach optimally adjusts the sampling
process to achieve optimal performance. We explore geographical relationships
among samples by distance-based sampling to maximize overall cumulative reward.
We apply ASR to the long-standing sampling problems in similarity-based loss
functions. Empirical results in information retrieval and clustering
demonstrate ASR's superb performance across different datasets. We also discuss
an engrossing phenomenon which we name as "ASR gravity well" in experiments
An Optimal Transport Approach to Deep Metric Learning (Student Abstract)
Capturing visual similarity among images is the core of many computer vision and pattern recognition tasks. This problem can be formulated in such a paradigm called metric learning. Most research in the area has been mainly focusing on improving the loss functions and similarity measures. However, due to the ignoring of geometric structure, existing methods often lead to sub-optimal results. Thus, several recent research methods took advantage of Wasserstein distance between batches of samples to characterize the spacial geometry. Although these approaches can achieve enhanced performance, the aggregation over batches definitely hinders Wasserstein distance's superior measure capability and leads to high computational complexity. To address this limitation, we propose a novel Deep Wasserstein Metric Learning framework, which employs Wasserstein distance to precisely capture the relationship among various images under ranking-based loss functions such as contrastive loss and triplet loss. Our method directly computes the distance between images, considering the geometry at a finer granularity than batch level. Furthermore, we introduce a new efficient algorithm using Sinkhorn approximation and Wasserstein measure coreset. The experimental results demonstrate the improvements of our framework over various baselines in different applications and benchmark datasets
Demystify the Gravity Well in the Optimization Landscape (Student Abstract)
We provide both empirical and theoretical insights to demystify the gravity well phenomenon in the optimization landscape. We start from describe the problem setup and theoretical results (an escape time lower bound) of the Softmax Gravity Well (SGW) in the literature. Then we move toward the understanding of a recent observation called ASR gravity well. We provide an explanation of why normal distribution with high variance can lead to suboptimal plateaus from an energy function point of view. We also contribute to the empirical insights of curriculum learning by comparison of policy initialization by different normal distributions. Furthermore, we provide the ASR escape time lower bound to understand the ASR gravity well theoretically. Future work includes more specific modeling of the reward as a function of time and quantitative evaluation of normal distribution’s influence on policy initialization