6,568 research outputs found
Deep Learning in the Automotive Industry: Applications and Tools
Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.Comment: 10 page
Creating a Reference Model for the Creative Industries – Evaluation of Configurable Event Driven Process Chains in Practice
The Australian screen business is facing new challenges to stay competitive. Increasing globalization on the one hand offers new opportunities for service-related companies but on the other hand raises the competition in drawing the attention of production companies. Motivated by a lack of process-centered management support, this work aims to deliver a starting point for an application of Business Process Management principles and practices within the Creative Industries to help facing the new challenges. I therefore present a reference process model for the post production part of screen business productions. To allow for comprehensive adaptation to project-specific scenarios or a company’s requirements the model is developed as Configurable Even-Driven Process Chain. The designated reference modeling language provides means to clearly specify process variations. In a second step I use the application in a real-life case to contribute to the empirical validation of the modeling language and to reflect on the relevance and completeness of the language
On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective
Our situated environment is full of uncertainty and highly dynamic, thus
hindering the widespread adoption of machine-led Intelligent Decision-Making
(IDM) in real world scenarios. This means IDM should have the capability of
continuously learning new skills and efficiently generalizing across wider
applications. IDM benefits from any new approaches and theoretical
breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the
barriers between tasks and applications. Recent research has well-examined
neural architecture, Transformer, as a backbone foundation model and its
generalization to various tasks, including computer vision, natural language
processing, and reinforcement learning. We therefore argue that a foundation
decision model (FDM) can be established by formulating various decision-making
tasks as a sequence decoding task using the Transformer architecture; this
would be a promising solution to advance the applications of IDM in more
complex real world tasks. In this paper, we elaborate on how a foundation
decision model improves the efficiency and generalization of IDM. We also
discuss potential applications of a FDM in multi-agent game AI, production
scheduling, and robotics tasks. Finally, through a case study, we demonstrate
our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters,
which achieves human-level performance over 453 tasks, including text
generation, images caption, video games playing, robotic control, and traveling
salesman problems. As a foundation decision model, DB1 would be a baby step
towards more autonomous and efficient real world IDM applications.Comment: 26 pages, 4 figure
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
The full potential of large pretrained models remains largely untapped in
control domains like robotics. This is mainly because of the scarcity of data
and the computational challenges associated with training or fine-tuning these
large models for such applications. Prior work mainly emphasizes effective
pretraining of large models for decision-making, with little exploration into
how to perform data-efficient continual adaptation of these models for new
tasks. Recognizing these constraints, we introduce TAIL (Task-specific Adapters
for Imitation Learning), a framework for efficient adaptation to new control
tasks. Inspired by recent advancements in parameter-efficient fine-tuning in
language domains, we explore efficient fine-tuning techniques -- e.g.,
Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to
adapt large pretrained models for new tasks with limited demonstration data.
Our extensive experiments in large-scale language-conditioned manipulation
tasks comparing prevalent parameter-efficient fine-tuning techniques and
adaptation baselines suggest that TAIL with LoRA can achieve the best
post-adaptation performance with only 1\% of the trainable parameters of full
fine-tuning, while avoiding catastrophic forgetting and preserving adaptation
plasticity in continual learning settings.Comment: 21 pages, 8 figures, 8 table
Audio Mastering as Musical Practice
This thesis examines audio mastering as musical communication. Tasks including loudness management, harmonic balance, denoising, phase alignment, monitoring, effects application, and administrative responsibilities are of central importance to mastering engineers. With the exception of administrative responsibilities, each of these tasks significantly shapes a record’s aesthetic character and physical makeup. These contributions – the final creative steps before an album’s release – demonstrate the mastering engineer’s role as a collaborative auteur in recorded musical communications
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
Audio Mastering as a Musical Competency
In this dissertation, I demonstrate that audio mastering is a musical competency by elucidating the most significant, and clearly audible, facets of this competence. In fact, the mastering process impacts traditionally valued musical aspects of records, such as timbre and dynamics. By applying the emerging creative scholarship method used within the field of music production studies, this dissertation will aid scholars seeking to hear and understand audio mastering by elucidating its core practices as musical endeavours. And, in so doing, I hope to enable increased clarity and accuracy in future scholarly discussions on the topic of audio mastering, as well as the end product of the mastering process: records.
Audio mastering produces a so-called master of a record, that is, a finished version of a record optimized for duplication and distribution via available formats (i.e, vinyl LP, audio cassette, compact disc, mp3, wav, and so on). This musical process plays a crucial role in determining how records finally sound, and it is not, as is so often inferred in research, the sole concern of a few technicians working in isolated rooms at a record label\u27s corporate headquarters. In fact, as Mark Cousins and Russ Hepworth-Sawyer (2013: 2) explain, nowadays “all musicians and engineers, to a lesser or greater extent, have to actively engage in the mastering process.” Thus, this dissertation clarifies the creative nature of audio mastering through an investigation of how mastering engineers hear records, and how they use technology to achieve the sonic goals they conceptualize
Synthetic Experience Replay
A key theme in the past decade has been that when large neural networks and
large datasets combine they can produce remarkable results. In deep
reinforcement learning (RL), this paradigm is commonly made possible through
experience replay, whereby a dataset of past experiences is used to train a
policy or value function. However, unlike in supervised or self-supervised
learning, an RL agent has to collect its own data, which is often limited.
Thus, it is challenging to reap the benefits of deep learning, and even small
neural networks can overfit at the start of training. In this work, we leverage
the tremendous recent progress in generative modeling and propose Synthetic
Experience Replay (SynthER), a diffusion-based approach to flexibly upsample an
agent's collected experience. We show that SynthER is an effective method for
training RL agents across offline and online settings, in both proprioceptive
and pixel-based environments. In offline settings, we observe drastic
improvements when upsampling small offline datasets and see that additional
synthetic data also allows us to effectively train larger networks.
Furthermore, SynthER enables online agents to train with a much higher
update-to-data ratio than before, leading to a significant increase in sample
efficiency, without any algorithmic changes. We believe that synthetic training
data could open the door to realizing the full potential of deep learning for
replay-based RL algorithms from limited data. Finally, we open-source our code
at https://github.com/conglu1997/SynthER.Comment: Published at NeurIPS, 202
Survival guide for road warriors : essentials for the mobile CPA
https://egrove.olemiss.edu/aicpa_guides/1233/thumbnail.jp
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