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Found 25 papers, 7 papers with code
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration
Recent advancements in deep reinforcement learning (RL) have demonstrated notable progress in sample efficiency, spanning both model-based and model-free paradigms.
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception
Furthermore, image syntheses from DetDiffusion can effectively augment training data, significantly enhancing downstream detection performance.
Uncertainty in Graph Neural Networks: A Survey
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
Disclosure and Mitigation of Gender Bias in LLMs
Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs.
kNN-ICL: Compositional Task-Oriented Parsing Generalization with Nearest Neighbor In-Context Learning
Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags.
DALA: A Distribution-Aware LoRA-Based Adversarial Attack against Language Models
DALA considers distribution shifts of adversarial examples to improve the attack's effectiveness under detection methods.
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction
Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text.
Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification
Then we merge the sentences selected for a specific aspect as the input for the summarizer to produce the aspect-based summary.
Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation
Large Language Models (LLMs) can generate biased and toxic responses.
Aperture Diffraction for Compact Snapshot Spectral Imaging
We demonstrate a compact, cost-effective snapshot spectral imaging system named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter sensor, requiring no additional physical footprint compared to common RGB cameras.
Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables
Furthermore, TAG-QA outperforms the end-to-end model T5 by 16% and 12% on BLEU-4 and PARENT F-score, respectively.
Named Entity Recognition via Machine Reading Comprehension: A Multi-Task Learning Approach
In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER.
Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions
Text segmentation tasks have a very wide range of application values, such as image editing, style transfer, watermark removal, etc. However, existing public datasets are of poor quality of pixel-level labels that have been shown to be notoriously costly to acquire, both in terms of money and time.
Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce
Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems.
DBFed: Debiasing Federated Learning Framework based on Domain-Independent
However, it brings challenges in information security and data security.
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation
Diffusion models have attracted significant attention due to the remarkable ability to create content and generate data for tasks like image classification.
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence.
Continuous Prompt Tuning Based Textual Entailment Model for E-commerce Entity Typing
In order to handle new entities in product titles and address the special language styles problem of product titles in e-commerce domain, we propose our textual entailment model with continuous prompt tuning based hypotheses and fusion embeddings for e-commerce entity typing.
Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization
The key issue is to track and estimate a sequence of $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots, g_m(\mathbf{w}))$ across iterations, where $\mathbf g(\mathbf{w})$ has $m$ blocks and it is only allowed to probe $\mathcal{O}(1)$ blocks to attain their stochastic values and Jacobians.
Explore Spatio-temporal Aggregation for Insubstantial Object Detection: Benchmark Dataset and Baseline
We endeavor on a rarely explored task named Insubstantial Object Detection (IOD), which aims to localize the object with following characteristics: (1) amorphous shape with indistinct boundary; (2) similarity to surroundings; (3) absence in color.
Projection-free Online Learning with Arbitrary Delays
Projection-free online learning, which eschews the projection operation via less expensive computations such as linear optimization (LO), has received much interest recently due to its efficiency in handling high-dimensional problems with complex constraints.
A Transformer-based Network for Deformable Medical Image Registration
Deformable medical image registration plays an important role in clinical diagnosis and treatment.
Optimal Algorithms for Stochastic Multi-Level Compositional Optimization
To address these limitations, we propose a Stochastic Multi-level Variance Reduction method (SMVR), which achieves the optimal sample complexity of $\mathcal{O}\left(1 / \epsilon^{3}\right)$ to find an $\epsilon$-stationary point for non-convex objectives.
Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text Kernel
Offline Chinese handwriting text recognition is a long-standing research topic in the field of pattern recognition.
Tunable ferroelectricity in hBN intercalated twisted double-layer graphene
Van der Waals (vdW) assembly of two-dimensional materials has been long recognized as a powerful tool to create unique systems with properties that cannot be found in natural compounds.
Mesoscale and Nanoscale Physics Materials Science
