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Found 32 papers, 14 papers with code
QARM: Quantitative Alignment Multi-Modal Recommendation at Kuaishou
In recent years, with the significant evolution of multi-modal large models, many recommender researchers realized the potential of multi-modal information for user interest modeling.
TODO: Enhancing LLM Alignment with Ternary Preferences
Standard alignment techniques, such as Direct Preference Optimization (DPO), often rely on the binary Bradley-Terry (BT) model, which can struggle to capture the complexities of human preferences -- particularly in the presence of noisy or inconsistent labels and frequent ties.
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
Causal disentanglement aims to learn about latent causal factors behind data, holding the promise to augment existing representation learning methods in terms of interpretability and extrapolation.
A Robust Anchor-based Method for Multi-Camera Pedestrian Localization
This paper addresses the problem of vision-based pedestrian localization, which estimates a pedestrian's location using images and camera parameters.
Causal Discovery with Fewer Conditional Independence Tests
We show that it is possible to a learn a coarser representation of the hidden causal graph with a polynomial number of tests.
Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning
Multi-modality spatio-temporal (MoST) data extends spatio-temporal (ST) data by incorporating multiple modalities, which is prevalent in monitoring systems, encompassing diverse traffic demands and air quality assessments.
Membership Testing in Markov Equivalence Classes via Independence Query Oracles
While learning involves the task of recovering the Markov equivalence class (MEC) of the underlying causal graph from observational data, the testing counterpart addresses the following critical question: Given a specific MEC and observational data from some causal graph, can we determine if the data-generating causal graph belongs to the given MEC?
SecFormer: Towards Fast and Accurate Privacy-Preserving Inference for Large Language Models
However, the application of SMPC in Privacy-Preserving Inference (PPI) for large language models, particularly those based on the Transformer architecture, often leads to considerable slowdowns or declines in performance.
Meek Separators and Their Applications in Targeted Causal Discovery
In our work, we focus on two such well-motivated problems: subset search and causal matching.
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
These results provide compelling evidence that our method has the potential to serve as a stepping stone for the development of causal foundation models.
NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
The development of TransRec has encountered multiple challenges, among which the lack of large-scale, high-quality transfer learning recommendation dataset and benchmark suites is one of the biggest obstacles.
An Image Dataset for Benchmarking Recommender Systems with Raw Pixels
Then, to show the effectiveness of the dataset's image features, we substitute the itemID embeddings (from IDNet) with a powerful vision encoder that represents items using their raw image pixels.
Ranked #1 on
Recommendation Systems
on PixelRec
Model Provenance via Model DNA
Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e. g., understanding where the model comes from, how it is trained, and how it is used).
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have paved the way for unprecedented opportunities for understanding the complexity of the brain and its emulation by computational systems.
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model.
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing
In contrast to existing studies that protect the data privacy of GPR via homomorphic encryption, differential privacy, or federated learning, our proposed method is more practical and can be used to preserve the data privacy of both the model inputs and outputs for various data-sharing scenarios (e. g., horizontally/vertically-partitioned data).
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in item recommendation tasks.
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Furthermore, we compare the performance of the TCF paradigm utilizing the most powerful LMs to the currently dominant ID embedding-based paradigm and investigate the transferability of this TCF paradigm.
Active Learning for Optimal Intervention Design in Causal Models
Here, we develop a causal active learning strategy to identify interventions that are optimal, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean.
Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise
Electromyography signals can be used as training data by machine learning models to classify various gestures.
Polarized deep diffractive neural network for classification, generation, multiplexing and de-multiplexing of orbital angular momentum modes
6 polarized OAM beams with identical total intensity and 8 cylinder vector beams with different topology charges also have been classified effectively.
Simple Contrastive Representation Adversarial Learning for NLP Tasks
In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs.
Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives
Background: Electronic Health Records (EHRs) contain rich information of patients' health history, which usually include both structured and unstructured data.
Subjective evaluation of traditional and learning-based image coding methods
Additionally, we utilize some typical and frequently used objective quality metrics to evaluate the coding methods in the experiment as comparison.
Matching a Desired Causal State via Shift Interventions
In particular, we show that our strategies may require exponentially fewer interventions than the previously considered approaches, which optimize for structure learning in the underlying causal graph.
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence
Information compression is essential to reduce communication cost in distributed optimization over peer-to-peer networks.
Optimizing the Whole-life Cost in End-to-end CNN Acceleration
With the heterogeneous functional layers that cannot be pro-cessed by the accelerators proposed for convolution layers only, modern end-to-end CNN acceleration so-lutions either transform the diverse computation into matrix/vector arithmetic, which loses data reuse op-portunities in convolution, or introduce dedicated functional unit to each kind of layer, which results in underutilization and high update expense.
Fast and Scalable Estimator for Sparse and Unit-Rank Higher-Order Regression Models
Because tensor data appear more and more frequently in various scientific researches and real-world applications, analyzing the relationship between tensor features and the univariate outcome becomes an elementary task in many fields.
Sparse and Low-Rank High-Order Tensor Regression via Parallel Proximal Method
Recently, tensor data (or multidimensional array) have been generated in many modern applications, such as functional magnetic resonance imaging (fMRI) in neuroscience and videos in video analysis.
Distributed Dual Gradient Tracking for Resource Allocation in Unbalanced Networks
Our key idea is the novel use of the distributed push-pull gradient algorithm (PPG) to solve the dual problem of the resource allocation problem.
Decentralized Stochastic Gradient Tracking for Non-convex Empirical Risk Minimization
We explicitly evaluate the convergence rate of DSGT with respect to the number of iterations in terms of algebraic connectivity of the network, mini-batch size, gradient variance, etc.
Efficient Private ERM for Smooth Objectives
For strongly convex and smooth objectives, we prove that gradient descent with output perturbation not only achieves nearly optimal utility, but also significantly improves the running time of previous state-of-the-art private optimization algorithms, for both $\epsilon$-DP and $(\epsilon, \delta)$-DP.
