【論文紹介】Spatial Temporal Graph Convolutional Networks for Skeleton-Based Acti...ddnpaa
(参考文献)Sijie Yan, Yuanjun Xiong, Dahua Lin.Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. Association for the Advancement of Artificial Intelligence (AAAI)2018
Semi supervised, weakly-supervised, unsupervised, and active learningYusuke Uchida
An overview of semi supervised learning, weakly-supervised learning, unsupervised learning, and active learning.
Focused on recent deep learning-based image recognition approaches.
Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
The document provides guidelines for an annotated bibliography assignment aimed at increasing nursing students' knowledge of leadership in nursing practice. Students will select five nurse leaders to research and write one-page summaries for each leader. Each summary must include the leader's roles and responsibilities, accomplishments, barriers to achieving goals, and knowledge gained from reading about the leader. The assignment will help prepare students for a poster presentation on nursing leadership.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
【論文紹介】Spatial Temporal Graph Convolutional Networks for Skeleton-Based Acti...ddnpaa
(参考文献)Sijie Yan, Yuanjun Xiong, Dahua Lin.Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. Association for the Advancement of Artificial Intelligence (AAAI)2018
Semi supervised, weakly-supervised, unsupervised, and active learningYusuke Uchida
An overview of semi supervised learning, weakly-supervised learning, unsupervised learning, and active learning.
Focused on recent deep learning-based image recognition approaches.
Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning.
2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term.
3. Variational autoencoders are discussed, where the approximate posterior is parameterized by a neural network and optimized to maximize the evidence lower bound. Latent variables are modeled as Gaussian distributions.
The document provides guidelines for an annotated bibliography assignment aimed at increasing nursing students' knowledge of leadership in nursing practice. Students will select five nurse leaders to research and write one-page summaries for each leader. Each summary must include the leader's roles and responsibilities, accomplishments, barriers to achieving goals, and knowledge gained from reading about the leader. The assignment will help prepare students for a poster presentation on nursing leadership.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
The document discusses different machine learning techniques including fuzzy logic, artificial neural networks, genetic algorithms, and case-based reasoning. It provides examples of how each technique works and potential applications. It notes that while these methods can be useful, they also have limitations such as lack of explainability and reliability issues for complex systems.
Using the Machine to predict TestabilityMiguel Lopez
This document discusses using machine learning to predict testability based on source code metrics. It begins with an introduction to the presenting organization and definitions of testability and machine learning concepts. It then shows how decision trees and other machine learning approaches could be used to predict testability levels (high, medium, low) based on source code metrics like number of interfaces, abstractness, and coupling. As an example, metrics from 9 Java packages were analyzed to build and test a predictive model in the Weka machine learning software. However, the document notes the initial model is simplistic and could be improved by incorporating more metrics related to factors in the testability fishbone diagram.
This publication is to help software engineering students to understand the basis in software testing. Software testing is an inevitable process in software development.
Artificial Intelligence in Neurology.pptxNeurologyKota
This document discusses the use of artificial intelligence and machine learning algorithms in neurology. It begins by introducing the topic and defining key terms like artificial intelligence, machine learning, and different types of machine learning algorithms. It then discusses how machine learning algorithms can be used for tasks like image classification in neurology, providing examples of analyzing retinal imaging, head CT scans, and MRI scans. The document also notes limitations of machine learning like bias, lack of generalizability, and the "black box" problem. It concludes by discussing specific applications of machine learning in areas like screening OCT images, predicting Alzheimer's progression, and detecting papilloedema and brain tumors.
The document discusses various machine learning concepts like model overfitting, underfitting, missing values, stratification, feature selection, and incremental model building. It also discusses techniques for dealing with overfitting and underfitting like adding regularization. Feature engineering techniques like feature selection and creation are important preprocessing steps. Evaluation metrics like precision, recall, F1 score and NDCG are discussed for classification and ranking problems. The document emphasizes the importance of feature engineering and proper model evaluation.
BioAssay Express: Creating and exploiting assay metadataPhilip Cheung
The challenge of accurately characterizing bioassays is a real pain point for many drug discovery organizations. Research has shown that some organizations have legacy assay collections exceeding 20,000 protocols, the great majority of which are not accurately characterized. This problem is compounded by the fact that many new protocol registrations are still not following FAIR (Findability, Accessibility, Interoperability, and Reusability) Data principles.
BioAssay Express is a tool focused on transforming the traditional protocol description from an unstructured free form text into a well-curated data store based upon FAIR Data principles. By using well-defined annotations for assays, the tool enables precise ontology based searches without having to resort to imprecise keyword searches.
This talk explores a number of new important features designed to help scientists accelerate the drug discovery process. Some example use-cases include: enabling drug repositioning projects; improving SAR models; identifying appropriate machine learning data sets; fine-tuning integrative-omic pathways;
An aspirational goal for our team is to build a metadata schema based on semantic web vocabularies that is comprehensive to the extent that the text description becomes optional. One of the many possibilities is to take the initial prospective ELN entry for a bioassay protocol and feed it directly to an automated instrument. While there are many challenges involved in creating the ELN-to-robot loop, we will provide some insights into our collaborations with UCSF automation experts.
In summary, the ability to quickly and accurately search or analyze bioassay data (public or internal) is a rate limiting problem in drug discovery. We will present the latest developments toward removing this bottleneck.
https://plan.core-apps.com/acs_sd2019/abstract/6f58993d-a716-49ad-9b09-609edde5a3f4
The document discusses several machine learning algorithms: artificial neural networks, naive Bayes classification, and decision trees. It provides examples of applying these algorithms to classify banking customers and compare their performance. Neural networks had the highest accuracy at 88.92% but the longest processing time of 8.01 seconds. Naive Bayes had the shortest processing time of 0.02 seconds but the lowest accuracy at 86.88%. Decision trees achieved 88.98% accuracy with a processing time of 0.04 seconds. The document also provides real-world examples of applying neural networks to tasks like ECG analysis, credit risk management, and environmental modeling.
Abstract—Combinatorial testing (also called interaction testing) is an effective specification-based test input generation technique. By now most of research work in combinatorial testing aims to propose novel approaches trying to generate test suites with minimum size that still cover all the pairwise, triple, or n-way combinations of factors. Since the difficulty of solving this problem is demonstrated to be NP-hard, existing approaches have been designed to generate optimal or near optimal combinatorial test suites in polynomial time. In this paper, we try to apply particle swarm optimization (PSO), a kind of meta-heuristic search technique, to pairwise testing (i.e. a special case of combinatorial testing aiming to cover all the pairwise combinations). To systematically build pairwise test suites, we propose two different PSO based algorithms. One algorithm is based on one-test-at-a-time strategy and the other is based on IPO-like strategy. In these two different algorithms, we use PSO to complete the construction of a single test. To successfully apply PSO to cover more uncovered pairwise combinations in this construction process, we provide a detailed description on how to formulate the search space, define the fitness function and set some heuristic settings. To verify the effectiveness of our approach, we implement these algorithms and choose some typical inputs. In our empirical study, we analyze the impact factors of our approach and compare our approach to other well-known approaches. Final empirical results show the effectiveness and efficiency of our approach.
This document discusses various techniques for machine learning when labeled training data is limited, including semi-supervised learning approaches that make use of unlabeled data. It describes assumptions like the clustering assumption, low density assumption, and manifold assumption that allow algorithms to learn from unlabeled data. Specific techniques covered include clustering algorithms, mixture models, self-training, and semi-supervised support vector machines.
This document describes an online over-sampling principal component analysis (osPCA) algorithm for detecting outliers in large datasets. Unlike prior PCA approaches, osPCA does not store the entire data matrix or covariance matrix, making it suitable for online or large-scale problems. It works by duplicating potential outlier instances instead of removing them to amplify their effect on the principal components. This allows osPCA to identify outliers by observing variations in the principal directions with and without each data point. The approach can also detect new outliers in an online setting by quickly updating the principal directions for new data.
Team 7 presented their findings on anomaly detection in credit card transactions. They tested several techniques including random forests with SMOTE oversampling, one class SVMs, and threshold tuning to optimize AUC. Their best model predicted 98% of fraud transactions while maintaining high precision and recall. They demonstrated their model on a live credit card fraud detection system and for analyzing single transactions.
Key Insights Of Using Deep Learning To Analyze Healthcare Data | Workshop Fro...Michael Batavia
In this presentation, I present how to properly discover, analyze and find trends in various types of healthcare data in order to utilize machine learning algorithms to predict future trends in the data. This presentation directly discusses the implications of data analysis in predicting benign and malignant cancers but the same techniques in this presentation can be applied to any other types of data in the real world.
For a more in-depth presentation, please watch the video presentation of this slideshow linked here: https://youtu.be/gXSl2iWcJ00
A case study that explains how quality of data is much better in case of online surveys, with guidelines on how sampling and non-sampling errors are eliminated.
The document discusses a framework for automatically testing agent-based systems using fault models. It proposes defining fault models that specify assumptions about when faults are likely to occur in the system under test. This allows an automated testing process to generate test cases, execute them, and identify any failures as existing faults. The framework extracts information from design documents to test individual units like plans without understanding their internal logic. It aims to provide comprehensive test coverage while reducing costs compared to manual testing. Defining fault models is meant to make the testing process more effective at revealing faults compared to existing techniques.
Testing is important because software errors can have serious consequences like customer bank balances being inflated by $763 billion or radiation therapy machines overexposing patients. Testing helps verify that software meets its specifications and functions as intended. There are two main types of testing: static testing which analyzes source code without running programs, and dynamic testing which executes programs to look for errors. It is difficult to exhaustively test all possible inputs for non-trivial programs, so test cases must strategically sample a small percentage of inputs to uncover many defects. Both black box and white box testing methods aim to design effective test cases.
Explains the concept of autovalidation that can be used to select predictive models with data from designed experiments where a true validation set is not available. Contains three case studies to demonstrate the approach
Lung cancer is a significant public health issue. So Early detection and diagnosis of lung cancer can significantly improve the survival rates of patients. In this presentation, we will discuss the development of a neural network for the prediction of lung cancer.
Machine learning workshop, session 4.
- Generalization in Machine Learning
- Overfitting and Underfitting
- Algorithms by Similarity
- Real Application
- People to follow
Similar to PR-190: A Baseline For Detecting Misclassified and Out-of-Distribution Examples In Neural Networks (20)
This document discusses permutation learning and describes DeepPermNet, a method for visual permutation learning using neural networks. DeepPermNet aims to predict permutation matrices that represent rank orders of attributes in images. It explores two strategies to address issues with directly predicting large numbers of permutation classes. The first strategy uses channel-wise concatenation but has problems with cheating. The second strategy reshapes the output and uses Sinkhorn iteration to produce a doubly stochastic matrix. The method is evaluated on a facial attributes dataset, achieving improved results over naive approaches based on metrics like Hamming similarity and Kendall-Tau similarity.
[Pr12] deep anomaly detection using geometric transformations강민국 강민국
This document proposes a method for deep anomaly detection using geometric transformations. It generates multiple transformed versions of images through translations, flips, and rotations to train a neural network. It then uses the network's softmax probabilities on the transformed images to calculate an anomaly score, with more anomalous images having lower average probabilities. The method is evaluated on CIFAR-10 by training on normal samples and testing to detect anomalies in separate test samples. In experiments, the pure version of anomaly detection is performed without labels for the test samples.
This document discusses self-supervised generative adversarial networks (SSGANs). It begins by explaining that training GANs is challenging due to the non-convex search problem and non-stationary environment. It then proposes that learning a conditional discriminator may help address this by making the learning problem easier. The document introduces SSGANs, which use self-supervision from image transformations to train the discriminator in a conditional manner. It concludes by discussing experiments using SSGANs on various datasets to generate images by training from scratch to convergence.
This document summarizes energy-based generative adversarial networks. It begins by explaining traditional energy-based models and vanilla GANs. It then introduces energy-based GANs, which use an encoder-decoder as the discriminator to output real and fake energies. The generator tries to minimize fake energy while the discriminator tries to maximize real energy and minimize fake energy. It provides the loss functions for the generator and discriminator. Finally, it discusses regularization techniques, potential applications to image generation, and experimental results comparing energy-based GANs to DCGANs on ImageNet datasets.
1. The Deep Feature Consistent Variational Autoencoder paper proposes using a perceptual loss function to improve over standard VAEs which can generate blurry images. The perceptual loss matches deep feature activations between real and generated images.
2. The model consists of an encoder that outputs mean and variance, a reparameterized decoder, and is trained with a KL divergence loss and perceptual loss on deep feature maps.
3. Experiments show the model generates higher quality images from latent vectors, can manipulate images through vector arithmetic, and the latent space supports facial attribute prediction tasks.
- Restricted Boltzmann Machine (RBM) is a type of neural network that learns a probability distribution over its inputs. It has two layers - a visible layer and a hidden layer.
- RBM uses a Boltzmann distribution to model the distribution of data, with the goal of explaining the distribution of the data. It can generate new data samples and learn the latent features of the input data.
- RBM training involves calculating partial derivatives of the log-likelihood function with respect to the parameters to update the parameters, rather than finding the exact solution. This involves computing conditional probabilities between the visible and hidden units.
This document discusses backpropagation, an algorithm for supervised learning of artificial neural networks using gradient descent. It provides definitions and history of backpropagation, and explains how to use it with three main points:
1) It uses simple chain rules to calculate derivatives between weights in different layers to update weights.
2) Preparations include defining a cost function and the derivative of the sigmoid activation function commonly used.
3) The weight updates are dependent on derivatives from previous layers, and both forward and backward paths must be considered to calculate some derivatives between weights. Gradient descent is then applied to renew the weights.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
PR-190: A Baseline For Detecting Misclassified and Out-of-Distribution Examples In Neural Networks
1. A Baseline For Detecting Misclassified and Out-of-Distribution
Examples In Neural Networks
PR-190
Kang, MinGuk
mingukkang1994@gmail.com
Sep., 01, 2019
https://arxiv.org/abs/1610.02136
4. Why Deep Neural Networks tend to be overconfident?
① Softmax Probabilities are computed
with the fast-growing exponential function
But… No Experimental Analysis
https://arxiv.org/pdf/1706.04599.pdf
Expected Calibration Error(ECE)
① Depth ↑
② Filters ↑
③ Batch Normalization 有
④ Weight Decay ↓
It remains future work to understand why these
trends affect calibration while improving accuracy.
5. Contributions of this Paper
1. They show the prediction probability of incorrect and out-of-distribution examples tends to be lower
than the prediction probability for correct examples.
2. These prediction probabilities form our detection baseline, and we demonstrate its efficacy through
various computer vision, natural language processing, and automatic speech recognition tasks.
3. They contribute one method which outperforms the baseline on some (but not all) tasks.
4. the designation of standard tasks and evaluation metrics for assessing the automatic detection of errors
and out-of-distribution examples.
6. Evaluation Metrics
In-distribution Fish: 99
Out-of-distribution Fish: 1
Cheating Neural Network: 99% accuracy!
So, Accuracy is not appropriate metric for out-of-distribution detection.
7. Evaluation Metrics
① AUROC(Area Under Receiver Operating Characteristic Curve) ① AUPR(Area Under Precision Recall Curve)
FPR(False Positive Rate):
𝐹𝑃
𝐹𝑃+𝑇𝑁
TPR(True Positive Rate):
𝑇𝑃
𝑇𝑃+𝐹𝑁
interpreted as the probability that a positive example has a greater
detector score/value than a negative example (Fawcett, 2005).
AUROC is not ideal when the positive class and negative class have
greatly differing base rates
Precision:
𝑇𝑃
𝑇𝑃+𝐹𝑃
Recall:
𝑇𝑃
𝑇𝑃+𝐹𝑁
interpreted as the probability that a positive example has a greater
detector score/value than a negative example (Fawcett, 2005).
AUROC is not ideal when the positive class and negative class have
greatly differing base rates
10. Experiments(NLP)
Same Phenomenon was discovered in the NLP! Sentiment Classification
Text Categorization
Automatic Speech Recognition
Experimental Results of Sentiment Classification
11. Improved Method
Abnormality Module
1. Train a normal classifier and append an auxiliary decoder
which reconstructs the input with in-distribution dataset.
2. Froze the blue layer.
3. Train red layers on clean and noised training examples.
Finally the sigmoid output of the red layers scores how normal the input is
13. Expected Calibration Error(ECE)
① Depth ↑
② Filters ↑
③ Batch Normalization 有
④ Weight Decay ↓
It remains future work to understand why these
trends affect calibration while improving accuracy.
On Calibration of Modern Neural Networks
(2017.06.14)
(2016.10.07)
14. A Simple Unified Framework for Detecting Out-of-
Distribution Samples And Adversarial Attacks
(2018.07.10)
Training Confidence-Calibrated Classifiers for detecting
Out-of-Distribution samples
(2017.11.26)
Train Generative Adversarial Networks to generate
Boundary Samples.
Class(k)
Probability
1/k
15. Deep Anomaly Detection with Outlier Exposure
(2018.12.11)
Utilize Realistic Outliers instead of boundary samples
Class(k)
Probability
1/k
In-distribution dataset Out-of-Distribution dataset
Thank You!