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Found 13 papers, 8 papers with code
Dynamically Anchored Prompting for Task-Imbalanced Continual Learning
We find that imbalanced tasks significantly challenge the capability of models to control the trade-off between stability and plasticity from the perspective of recent prompt-based continual learning methods.
Clustered Embedding Learning for Recommender Systems
However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up.
EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle
In recent years, the rapid development of deep learning has brought great advancements to image and video segmentation methods based on neural networks.
Visuo-Tactile Transformers for Manipulation
Learning representations in the joint domain of vision and touch can improve manipulation dexterity, robustness, and sample-complexity by exploiting mutual information and complementary cues.
On Provably Robust Meta-Bayesian Optimization
We prove that both algorithms are asymptotically no-regret even when some or all previous tasks are dissimilar to the current task, and show that RM-GP-UCB enjoys a better theoretical robustness than RM-GP-TS.
Multi-treatment Effect Estimation from Biomedical Data
This work proposes the M3E2, a multi-task learning neural network model to estimate the effect of multiple treatments.
EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow
In addition, with the proposed method, we develop an efficient interactive segmentation tool for practical data annotation tasks.
Ranked #2 on
Interactive Segmentation
on PASCAL VOC
(NoC@85 metric)
Neural Ensemble Search via Bayesian Sampling
Unfortunately, these NAS algorithms aim to select only one single well-performing architecture from their search spaces and thus have overlooked the capability of neural network ensemble (i. e., an ensemble of neural networks with diverse architectures) in achieving improved performance over a single final selected architecture.
Convolutional Normalizing Flows for Deep Gaussian Processes
Deep Gaussian processes (DGPs), a hierarchical composition of GP models, have successfully boosted the expressive power of their single-layer counterpart.
Gated Transformer Networks for Multivariate Time Series Classification
In this work, we explored a simple extension of the current Transformer Networks with gating, named Gated Transformer Networks (GTN) for the multivariate time series classification problem.
Meta-Learning with Implicit Processes
This paper presents a novel implicit process-based meta-learning (IPML) algorithm that, in contrast to existing works, explicitly represents each task as a continuous latent vector and models its probabilistic belief within the highly expressive IP framework.
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games
This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2).
Implicit Posterior Variational Inference for Deep Gaussian Processes
This paper presents an implicit posterior variational inference (IPVI) framework for DGPs that can ideally recover an unbiased posterior belief and still preserve time efficiency.
