Search Results for author:
Found 17 papers, 14 papers with code
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead.
GradSafe: Detecting Unsafe Prompts for LLMs via Safety-Critical Gradient Analysis
In this study, we propose GradSafe, which effectively detects unsafe prompts by scrutinizing the gradients of safety-critical parameters in LLMs.
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs.
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Based on the evaluation, our work makes a key analysis of the underlying reason for the success of the attack, namely the inability of LLMs to distinguish between instructions and external content and the absence of LLMs' awareness to not execute instructions within external content.
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored.
LLMRec: Benchmarking Large Language Models on Recommendation Task
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
Attention Calibration for Transformer-based Sequential Recommendation
Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations.
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method.
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Specifically, we first reserve a short trajectory of global model snapshots on the server.
Equivariant Contrastive Learning for Sequential Recommendation
Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Since at each round, the number of tunable parameters optimized on the server side equals the number of participating clients (thus independent of the model size), we are able to train a global model with massive parameters using only a small amount of proxy data (e. g., around one hundred samples).
Optimizing Image Compression via Joint Learning with Denoising
The key is to transform the original noisy images to noise-free bits by eliminating the undesired noise during compression, where the bits are later decompressed as clean images.
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Based on this observation, we intuitively propose to remove the GNN propagation part, while the readout module will take on more responsibility in the model reasoning process.
Decoupled Side Information Fusion for Sequential Recommendation
Motivated by this, we propose Decoupled Side Information Fusion for Sequential Recommendation (DIF-SR), which moves the side information from the input to the attention layer and decouples the attention calculation of various side information and item representation.
IICNet: A Generic Framework for Reversible Image Conversion
Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e. g., short videos) and an embedding image, where the original content can be restored from the embedding when necessary.
Enhanced Invertible Encoding for Learned Image Compression
Although deep learning based image compression methods have achieved promising progress these days, the performance of these methods still cannot match the latest compression standard Versatile Video Coding (VVC).
Predict Future Sales using Ensembled Random Forests
This is a method report for the Kaggle data competition 'Predict future sales'.
