Search Results for author:
Found 16 papers, 6 papers with code
Think Twice, Click Once: Enhancing GUI Grounding via Fast and Slow Systems
This structured decomposition enables systematic understanding of both interface layouts and visual relationships.
GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
GaVaMoE introduces two key components: (1) a rating reconstruction module that employs Variational Autoencoder (VAE) with a Gaussian Mixture Model (GMM) to capture complex user-item collaborative preferences, serving as a pre-trained multi-gating mechanism; and (2) a set of fine-grained expert models coupled with the multi-gating mechanism for generating highly personalized explanations.
Younger: The First Dataset for Artificial Intelligence-Generated Neural Network Architecture
Designing and optimizing neural network architectures typically requires extensive expertise, starting with handcrafted designs and then manual or automated refinement.
Towards Large-Scale Training of Pathology Foundation Models
Driven by the recent advances in deep learning methods and, in particular, by the development of modern self-supervised learning algorithms, increased interest and efforts have been devoted to build foundation models (FMs) for medical images.
AGIBench: A Multi-granularity, Multimodal, Human-referenced, Auto-scoring Benchmark for Large Language Models
Instead of a collection of blended questions, AGIBench focuses on three typical ability branches and adopts a four-tuple <ability branch, knowledge, difficulty, modal> to label the attributes of each question.
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task.
Quality at the Tail of Machine Learning Inference
The study unveils a counterintuitive revelation: deep learning inference quality exhibits fluctuations due to inference time.
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP
We suggest to use modern Hopfield networks to tackle the problem of explaining away.
A remark on a paper of Krotov and Hopfield [arXiv:2008.06996]
In their recent paper titled "Large Associative Memory Problem in Neurobiology and Machine Learning" [arXiv:2008. 06996] the authors gave a biologically plausible microscopic theory from which one can recover many dense associative memory models discussed in the literature.
HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality.
Image Classification
Performance
AIBench Scenario: Scenario-distilling AI Benchmarking
We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
AIBench: An Industry Standard Internet Service AI Benchmark Suite
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
Random Forest Missing Data Algorithms
Using a large, diverse collection of data sets, performance of various RF algorithms was assessed under different missing data mechanisms.
