Search Results for author:
Found 22 papers, 4 papers with code
Optimizing Singular Spectrum for Large Language Model Compression
In this work, we introduce SoCo (Singular spectrum optimization for large language model Compression), a novel compression framework that learns to rescale the decomposed components of SVD in a data-driven manner.
HiMix: Reducing Computational Complexity in Large Vision-Language Models
This is inspired by a reassessment of the efficiency of vision and language information transmission in the language decoder of LVLMs.
Manga Generation via Layout-controllable Diffusion
However, there are few studies on generating multi-panel Manga (Japanese comics) solely based on plain text.
Pear: Pruning and Sharing Adapters in Visual Parameter-Efficient Fine-Tuning
Adapters have been widely explored to alleviate computational and storage costs when fine-tuning pretrained foundation models.
Low-Rank Interconnected Adaptation across Layers
Low-rank adaptation (LoRA) is a powerful parameter-efficient fine-tuning method that utilizes low-rank projectors $A$ and $B$ to learn weight updates $\Delta W$ for adaptation targets $W$.
Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks
Additionally, given the different responsiveness of heads to diverse visual tasks, our proposed method dynamically activates a subset of the approximated heads that are tailored to the current task.
Side Information-Driven Session-based Recommendation: A Survey
Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance.
MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation
Then we learn a semantic representation of MeKB for the cross-domain recommendation.
Retrieval-based Knowledge Augmented Vision Language Pre-training
With the recent progress in large-scale vision and language representation learning, Vision Language Pre-training (VLP) models have achieved promising improvements on various multi-modal downstream tasks.
Towards High-Order Complementary Recommendation via Logical Reasoning Network
Finally, we further propose a hybrid network that is jointly optimized for learning a more generic product representation.
A Neural Vocoder Based Packet Loss Concealment Algorithm
The packet loss problem seriously affects the quality of service in Voice over IP (VoIP) sceneries.
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.
Robust Federated Learning for Neural Networks
In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.
GAN-based Recommendation with Positive-Unlabeled Sampling
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i. e., it has deteriorated performance on adversarial examples when the model is deployed.
DA4AD: End-to-End Deep Attention-based Visual Localization for Autonomous Driving
We present a visual localization framework based on novel deep attention aware features for autonomous driving that achieves centimeter level localization accuracy.
Learning Efficient Video Representation with Video Shuffle Networks
To this end, we propose the video shuffle, a parameter-free plug-in component that efficiently reallocates the inputs of 2D convolution so that its receptive field can be extended to the temporal dimension.
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods.
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching
The unprecedented demand for large amount of data has catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to enlist label information both effectively and efficiently.
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications.
Modelling Sentence Pairs with Tree-structured Attentive Encoder
We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs.
