This research focuses on a system, known as ATPP, based on deep marked temporal point processes, designed for predicting mobile app usage patterns. By leveraging deep reinforcement learning frameworks and context-aware modules, the system aims to predict the next app a user will open, along with its

0 views • 24 slides

Explore the innovative concept of deep hedging and heat rate options introduced by Mark Higgins, focusing on improved hedging strategies through deep learning and optimization techniques. Discover the shift from traditional risk-neutral pricing to a more dynamic approach that utilizes neural network

0 views • 16 slides

The Next Space Technologies for Exploration Partnerships (NextSTEP) program, initiated in 2015, focused on enhancing deep space habitation capabilities through a public-private partnership. The Modular ECLSS Effort aimed to develop adaptable ECLSS systems for various exploration missions. It involve

0 views • 14 slides

Delve into the world of deep learning with a focus on utilizing GPUs for enhanced performance. Explore topics like neural networks, TensorFlow, PyTorch, and distributed training. Learn how deep learning algorithms process data, optimize weights and biases, and predict outcomes through training loops

4 views • 98 slides

Unleash your inner child at Deep Creek Lake! Beyond the serenity of nature and outdoor thrills, Deep Creek Lake offers a haven for family fun. Deep Creek Lake rentals with spacious living areas and game rooms provide the perfect space for creating lasting memories. Splash together at the lake's sand

2 views • 4 slides

Explain Learning is at the forefront of this movement, offering a comprehensive e-learning platform designed to simplify concepts and empower students to excel in their online learning journeys. Know more \/\/explainlearning.com\/blog\/explain-learning-e-learning-platform-simplifies-concepts\/

1 views • 4 slides

Join Shirley Ho at the ACM Learning event to delve into the world of symbolic equations with deep learning. Discover insights on leveraging deep learning for symbolic equations and engage in a knowledge-packed session tailored for scientists, programmers, designers, and managers.

0 views • 5 slides

Deep Transfer Learning and Multi-task Learning involve transferring knowledge from a source domain to a target domain, benefiting tasks such as image classification, sentiment analysis, and time series prediction. Taxonomies of Transfer Learning categorize approaches like model fine-tuning, multi-ta

1 views • 26 slides

Lujia Chen, a Postdoc Associate at the University of Pittsburgh, focuses on developing deep learning models for precision oncology. By utilizing machine learning, especially deep learning models, Chen aims to identify cancer signaling pathways, predict drug sensitivities, and personalize cancer trea

1 views • 5 slides

Delve into the world of deep reinforcement learning on the road to advanced AI systems like Skynet. Explore topics ranging from Markov Decision Processes to solving MDPs, value functions, and tabular solutions. Discover the paradigm of supervised, unsupervised, and reinforcement learning in various

0 views • 24 slides

Explore the intersection of deep learning and biotechnology, focusing on Word2Vec and its applications in protein structure prediction. Understand the transformation from discrete to continuous space, the challenges of traditional word representation methods, and the implications for computational l

0 views • 28 slides

General cultivation concepts for fodder production include primary and secondary tillage practices. Primary tillage consists of deep ploughing, subsoiling, and year-round tillage. Deep tillage is essential for deep-rooted crops while subsoiling breaks hard pans in the soil. Year-round tillage involv

0 views • 19 slides

Explore the concept of stacking Restricted Boltzmann Machines (RBMs) to learn hierarchical features in deep neural networks. By training layers of features directly from pixels and iteratively learning features of features, we can enhance the variational lower bound on log probability of generating

0 views • 39 slides

A comprehensive discussion on various topics such as Local Differential Privacy (LDP), Statistical Query Model, PAC learning, Margin Complexity, and Known Results in the context of machine learning. It covers concepts like separation, non-interactive learning, error bounds, and the efficiency of lea

0 views • 14 slides

Belgrade Metropolitan University (BMU) utilizes the Learning Activity Management System (LAMS) to enhance the learning process by integrating learning objects with various activities. This system allows for complex learning processes, mixing learning objects with LAMS activities effectively. The pro

4 views • 16 slides

Explore the guidelines for submitting a project proposal in the course ECE 6504 - Deep Learning for Perception. Learn about the necessary information required for the proposal webpage, project categories, main deliverables, and milestones. Understand the expectations regarding project teams, softwar

0 views • 9 slides

Deep learning, particularly deep neural networks (DNNs), has revolutionized machine learning with its high accuracy rates. However, in adversarial settings, adversaries can manipulate DNNs by crafting adversarial samples to force misclassification. Such attacks pose risks in various applications, in

0 views • 38 slides

EU Space Surveillance and Tracking program involves five European nations collaborating to assess and reduce risks to European spacecraft, provide early warnings for re-entries and space debris, and prevent space debris proliferation. Available deep space sensors, such as optical telescopes, are uti

1 views • 8 slides

Laser cutting is a fast and precise method, but predicting defects can be challenging. This study explores using Deep Learning to model and forecast laser cutting defects based on parameters. Topics include introduction to laser cutting, deep learning, imaging, and conclusions.

2 views • 20 slides

Detection of coughs and sneezes plays a crucial role in assessing an individual's health condition. This project by Group 71 focuses on real-time detection using deep learning techniques to analyze audio data from various datasets. The use of deep learning models like CNN and CRNN showcases improved

0 views • 15 slides

Insights from various lectures and discussions focusing on deep learning, reinforcement learning, and advancements in AI. Emphasis on moving beyond input-output views to richer internal representations and the integration of deep learning with symbolic reasoning. Highlighting the success in sensory

0 views • 8 slides

Addressing the disconnect between conceptual knowledge and applied competencies in education, the Curriculum Design Coherence Model offers a structured framework to create coherence for deep learning. Developed in response to educational challenges such as skills versus knowledge imbalance and fragm

1 views • 28 slides

Machine learning has evolved significantly over the decades, driven by concepts like Neural Networks, Reinforcement Learning, and Deep Learning. This technology enables machines to learn from past data to make predictions. Activities in machine learning involve data exploration, preparation, model t

0 views • 16 slides

Delve into the world of deep learning with Podolski's presentation slides covering topics like motivation, neural networks, Andrew Ng's perspectives, neuron types, and the essence of feature representation in deep learning algorithms.

0 views • 24 slides

PipeSwitch is a solution that enables fast and efficient context switching for deep learning applications, aiming to multiplex multiple DL apps on GPUs with minimal latency. It addresses the challenges of low GPU cluster utilization, high context switching overhead, and drawbacks of existing solutio

0 views • 46 slides

Introduction to the world of deep learning with Keras, a popular deep learning library developed by François Chollet. Learn about Keras, Theano, TensorFlow, and how to train neural networks for tasks like handwriting digit recognition using the MNIST dataset. Explore different activation functions,

0 views • 17 slides

This study introduces DA-MUSIC, a hybrid approach combining traditional MUSIC algorithm with deep learning for robust Direction of Arrival (DOA) estimation in seismic arrays. The methodology improves resolution and handles broadband signals effectively. Utilizing non-synthetic seismic data collected

0 views • 4 slides

This presentation by Martha Büttner at the AI for Dentistry Symposium delves into current challenges in dental deep learning, highlighting issues like data sharing, annotation bottlenecks, and comparability gaps. The talk proposes a solution through Federated Learning, showcasing a project on Tooth

0 views • 17 slides

Exploring the evolution of machine learning and deep learning in artificial intelligence through neural networks, with insights on supervised, unsupervised, and reinforcement learning. Learn about recommended resources like Java Weka and Python scikit-learn for data mining tasks. Delve into advancem

0 views • 16 slides

Explore the differences between awake and deep extubation techniques in anesthesia practice, including learning objectives, stages of anesthesia, safe execution, risks, benefits, appropriate candidates, and management of airway complications. Understand the criteria for awake extubation and indicati

0 views • 22 slides

In this lesson plan, students will delve into the world of sports and deep tissue massage, learning about the theoretical aspects, hands-on techniques, and graded events involved. The content covers classroom rules, the introduction to sports and deep tissue massage, an overview of the segment class

0 views • 25 slides

Utilizing deep learning facilitated microscopy, a research team led by Hening Cui from Columbia University aims to dissect durable resistance to plant diseases. The project focuses on segmenting hyphal networks of fungal and host plant cells using a deep convolutional neural network architecture cal

0 views • 13 slides

Quantum deep learning explores the potential of using quantum computing to address challenges in artificial intelligence, focusing on learning complex representations for tough AI problems. The quest is to automatically learn representations at both low and high levels, leveraging terabytes of web d

0 views • 14 slides

Code smells indicate code quality problems and the need for refactoring. This paper introduces a hybrid model for multi-label code smell detection using deep learning, achieving better results on Java projects from Github. The model extracts multi-level code representation and applies deep learning

0 views • 10 slides

Discover new hardware architectures designed for efficient deep neural network processing, including SCNN accelerators for compressed-sparse Convolutional Neural Networks. Learn about convolution operations, memory size versus access energy, dataflow decisions for reuse, and Planar Tiled-Input Stati

0 views • 23 slides

Exploring gradient-free and derivative-free optimization methods for deep learning, including insights on search space of deep networks and alternative approaches like ant colony optimization and simulated annealing. Emphasizes the importance of architecture and simpler training methods for improved

0 views • 4 slides

This presentation discusses the requirements and procedures for conducting economic analyses in deep-draft navigation, particularly focusing on the National Economic Development (NED) criteria. It covers concepts, procedural steps, historical/existing conditions, sources of navigation and data, and

0 views • 12 slides

This project aims to leverage deep learning for math-entity representation learning, math semantics extraction, and application development in the fields of mathematics and natural language processing. The long-term objectives include semantic enrichment of math expressions, conversion of math to co

2 views • 29 slides

Dive into the world of deep learning and artificial intelligence through Microsoft Research's exploration of new-generation models and methodologies for advancing AI. Topics covered include computational neuroscience, deep neural networks, vision and speech recognition, as well as the application of

0 views • 19 slides

Classic machine learning has limitations such as isolated single-task learning and closed-world assumptions. Lifelong machine learning aims to overcome these limitations by enabling models to continuously learn and adapt to new data. This is crucial for dynamic environments like chatbots and self-dr

0 views • 32 slides