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Found 35 papers, 5 papers with code
Establishing Rigorous and Cost-effective Clinical Trials for Artificial Intelligence Models
We envision DC-AI RCTs and VC-MedAI as pivotal advancements, presenting innovative and transformative evaluation methodologies for AI models in clinical practice, offering a preclinical-like setting mirroring conventional medicine, and reshaping development paradigms in a cost-effective and fast-iterative manner.
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.
AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI
To establish a unified evaluation framework for video generation tasks, our benchmark includes 11 metrics spanning four dimensions to assess algorithm performance.
SAIBench: A Structural Interpretation of AI for Science Through Benchmarks
Artificial Intelligence for Science (AI4S) is an emerging research field that utilizes machine learning advancements to tackle complex scientific computational issues, aiming to enhance computational efficiency and accuracy.
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.
Hierarchical Masked 3D Diffusion Model for Video Outpainting
Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames.
Does AI for science need another ImageNet Or totally different benchmarks? A case study of machine learning force fields
This suite of metrics has demonstrated a better ability to assess a model's performance in real-world scientific applications, in contrast to traditional AI benchmarking methodologies.
IterLara: A Turing Complete Algebra for Big Data, AI, Scientific Computing, and Database
The study of \textsc{Lara}'s expressive ability reports that it can represent relational algebra and most linear algebra operations.
OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis
Alzheimer's disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients' medical treatment and care.
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis
Thus, the AD diagnosis task is tangled and coupled with the diagnosis strategy formulation.
CMLCompiler: A Unified Compiler for Classical Machine Learning
Without a unified framework, the hybrid deployments of deep learning (DL) and CML also suffer from severe performance and portability issues.
Quality at the Tail of Machine Learning Inference
The study unveils a counterintuitive revelation: deep learning inference quality exhibits fluctuations due to inference time.
ToL: A Tensor of List-Based Unified Computation Model
This article presents a unified computation model with generalized expression ability and a concise set of primitive operators for programming high-level algorithms.
SAIBench: Benchmarking AI for Science
Scientific research communities are embracing AI-based solutions to target tractable scientific tasks and improve research workflows.
Heterologous Normalization
Specifically, it calculates the mean like Batch Normalization to maintain the advantage of Batch Normalization.
OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system.
Shift-and-Balance Attention
The attention branch is gated using the Sigmoid function and multiplied by the feature map's trunk branch.
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
FLBench: A Benchmark Suite for Federated Learning
The goal is to build a machine learning model from the data sets distributed on multiple devices so-called an isolated data island, while keeping their data secure and private.
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN).
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains.
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.
Extended Batch Normalization
To alleviate the problem caused by small batch size, extended batch normalization computes the standard deviation along the (N, C, H, W) dimensions, thus enlarges the number of samples from which the standard deviation is computed.
Ranked #61 on
Image Classification
on STL-10
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.
BenchCouncil's View on Benchmarking AI and Other Emerging Workloads
This paper outlines BenchCouncil's view on the challenges, rules, and vision of benchmarking modern workloads like Big Data, AI or machine learning, and Internet Services.
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.
Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking
In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues.
Performance Distributed, Parallel, and Cluster Computing
LoadCNN: A Low Training Cost Deep Learning Model for Day-Ahead Individual Residential Load Forecasting
As a consequence, the deep learning methods are difficult to be popularized and applied in the real smart grid environment.
HPC AI500: A Benchmark Suite for HPC AI Systems
In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.
A Semantic-based Medical Image Fusion Approach
Medical image fusion is a promising approach to providing overall information from medical images of different modalities.
A new direction to promote the implementation of artificial intelligence in natural clinical settings
Artificial intelligence (AI) researchers claim that they have made great `achievements' in clinical realms.
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.
Deep Convolutional Neural Networks for Anomaly Event Classification on Distributed Systems
The increasing popularity of server usage has brought a plenty of anomaly log events, which have threatened a vast collection of machines.
Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks
We compare cosine normalization with batch, weight and layer normalization in fully-connected neural networks as well as convolutional networks on the data sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN.
