274 research outputs found
MARIO: Model Agnostic Recipe for Improving OOD Generalization of Graph Contrastive Learning
In this work, we investigate the problem of out-of-distribution (OOD)
generalization for unsupervised learning methods on graph data. This scenario
is particularly challenging because graph neural networks (GNNs) have been
shown to be sensitive to distributional shifts, even when labels are available.
To address this challenge, we propose a \underline{M}odel-\underline{A}gnostic
\underline{R}ecipe for \underline{I}mproving \underline{O}OD generalizability
of unsupervised graph contrastive learning methods, which we refer to as MARIO.
MARIO introduces two principles aimed at developing distributional-shift-robust
graph contrastive methods to overcome the limitations of existing frameworks:
(i) Information Bottleneck (IB) principle for achieving generalizable
representations and (ii) Invariant principle that incorporates adversarial data
augmentation to obtain invariant representations. To the best of our knowledge,
this is the first work that investigates the OOD generalization problem of
graph contrastive learning, with a specific focus on node-level tasks. Through
extensive experiments, we demonstrate that our method achieves state-of-the-art
performance on the OOD test set, while maintaining comparable performance on
the in-distribution test set when compared to existing approaches. The source
code for our method can be found at: https://github.com/ZhuYun97/MARIOComment: 20 pages, 15 figure
An Efficient Threshold-Driven Aggregate-Label Learning Algorithm for Multimodal Information Processing
The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks
A Focused Study on Sequence Length for Dialogue Summarization
Output length is critical to dialogue summarization systems. The dialogue
summary length is determined by multiple factors, including dialogue
complexity, summary objective, and personal preferences. In this work, we
approach dialogue summary length from three perspectives. First, we analyze the
length differences between existing models' outputs and the corresponding human
references and find that summarization models tend to produce more verbose
summaries due to their pretraining objectives. Second, we identify salient
features for summary length prediction by comparing different model settings.
Third, we experiment with a length-aware summarizer and show notable
improvement on existing models if summary length can be well incorporated.
Analysis and experiments are conducted on popular DialogSum and SAMSum datasets
to validate our findings.Comment: Preprint version - ICASSP submissio
LiDAR-Generated Images Derived Keypoints Assisted Point Cloud Registration Scheme in Odometry Estimation
Keypoint detection and description play a pivotal role in various robotics
and autonomous applications including visual odometry (VO), visual navigation,
and Simultaneous localization and mapping (SLAM). While a myriad of keypoint
detectors and descriptors have been extensively studied in conventional camera
images, the effectiveness of these techniques in the context of LiDAR-generated
images, i.e. reflectivity and ranges images, has not been assessed. These
images have gained attention due to their resilience in adverse conditions such
as rain or fog. Additionally, they contain significant textural information
that supplements the geometric information provided by LiDAR point clouds in
the point cloud registration phase, especially when reliant solely on LiDAR
sensors. This addresses the challenge of drift encountered in LiDAR Odometry
(LO) within geometrically identical scenarios or where not all the raw point
cloud is informative and may even be misleading. This paper aims to analyze the
applicability of conventional image key point extractors and descriptors on
LiDAR-generated images via a comprehensive quantitative investigation.
Moreover, we propose a novel approach to enhance the robustness and reliability
of LO. After extracting key points, we proceed to downsample the point cloud,
subsequently integrating it into the point cloud registration phase for the
purpose of odometry estimation. Our experiment demonstrates that the proposed
approach has comparable accuracy but reduced computational overhead, higher
odometry publishing rate, and even superior performance in scenarios prone to
drift by using the raw point cloud. This, in turn, lays a foundation for
subsequent investigations into the integration of LiDAR-generated images with
LO. Our code is available on GitHub:
https://github.com/TIERS/ws-lidar-as-camera-odom
Progress in the seasonal variations of blood lipids: a mini-review.
The seasonal variations of blood lipids have recently gained increasing interest in this field of lipid metabolism. Elucidating the seasonal patterns of blood lipids is particularly helpful for the prevention and treatment of cardiovascular and cerebrovascular diseases. However, the previous results remain controversial and the underlying mechanisms are still unclear. This mini-review is focused on summarizing the literature relevant to the seasonal variability of blood lipid parameters, as well as on discussing its significance in clinical diagnoses and management decisions
- …