Search Results for author:
Found 13 papers, 9 papers with code
Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization
The emergence of large Vision Language Models (VLMs) has broadened the scope and capabilities of single-modal Large Language Models (LLMs) by integrating visual modalities, thereby unlocking transformative cross-modal applications in a variety of real-world scenarios.
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation
Our fMRLRec captures item features at different granularities, learning informative representations for efficient recommendation across multiple dimensions.
Your Causal Self-Attentive Recommender Hosts a Lonely Neighborhood
In the context of sequential recommendation, a pivotal issue pertains to the comparative analysis between bi-directional/auto-encoding (AE) and uni-directional/auto-regressive (AR) attention mechanisms, where the conclusions regarding architectural and performance superiority remain inconclusive.
LLMs for User Interest Exploration in Large-scale Recommendation Systems
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel user interests.
Linear Recurrent Units for Sequential Recommendation
State-of-the-art sequential recommendation relies heavily on self-attention-based recommender models.
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback.
Quantized Low-Rank Multivariate Regression with Random Dithering
Moreover, we extend our results to a low-rank regression model with matrix responses.
Amortized Probabilistic Detection of Communities in Graphs
While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requiring knowledge of the number of communities in advance, in addition to lacking a proper probabilistic formulation to handle uncertainty.
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates.
Neural Permutation Processes
We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects.
Spike Sorting using the Neural Clustering Process
We present a novel approach to spike sorting for high-density multielectrode probes using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering.
Neural Clustering Processes
Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces.
High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures
In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets.
