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Found 14 papers, 0 papers with code
Adversarial Domain Adaptation Being Aware of Class Relationships
In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.
Consensus Monte Carlo for Random Subsets using Shared Anchors
Motivated by three case studies, we focus on clustering induced by a Dirichlet process mixture sampling model, inference under an Indian buffet process prior with a binomial sampling model, and with a categorical sampling model.
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks
To infer causal relationships in zero-inflated count data, we propose a new zero-inflated Poisson Bayesian network (ZIPBN) model.
Ordinal Causal Discovery
Causal discovery for purely observational, categorical data is a long-standing challenging problem.
Efficient Off-Policy Reinforcement Learning via Brain-Inspired Computing
Our evaluation shows QHD capability for real-time learning, providing 34. 6 times speedup and significantly better quality of learning than DQN.
Efficient Personalized Learning for Wearable Health Applications using HyperDimensional Computing
Considering the significant role of wearable devices in monitoring human body parameters, on-device learning can be utilized to build personalized models for behavioral and physiological patterns, and provide data privacy for users at the same time.
Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation
Causal discovery for quantitative data has been extensively studied but less is known for categorical data.
Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data
We consider the problem of clustering grouped data with possibly non-exchangeable groups whose dependencies can be characterized by a known directed acyclic graph.
A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission
To enhance the framework's performance, the training process is customized and a "lazy" sensor deactivation strategy utilizing temporal information is introduced.
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning
Drawing inspiration from the outstanding learning capability of our human brains, Hyperdimensional Computing (HDC) emerges as a novel computing paradigm, and it leverages high-dimensional vector presentation and operations for brain-like lightweight Machine Learning (ML).
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
When conducting cross-models and cross-platforms comparison, HDReason yields an average 4. 2x higher performance and 3. 4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection
Nevertheless, the naive application of VLMs leads to sub-optimal quality, due to the misalignment between embeddings of object images and their visual attributes, which are mainly adjective phrases.
EcoSense: Energy-Efficient Intelligent Sensing for In-Shore Ship Detection through Edge-Cloud Collaboration
Detecting marine objects inshore presents challenges owing to algorithmic intricacies and complexities in system deployment.
Generalized Criterion for Identifiability of Additive Noise Models Using Majorization
In this paper, we introduce a novel identifiability criterion for DAGs that places constraints on the conditional variances of additive noise models.
