6 research outputs found
Quantifying Human Bias and Knowledge to guide ML models during Training
This paper discusses a crowdsourcing based method that we designed to
quantify the importance of different attributes of a dataset in determining the
outcome of a classification problem. This heuristic, provided by humans acts as
the initial weight seed for machine learning models and guides the model
towards a better optimal during the gradient descent process. Often times when
dealing with data, it is not uncommon to deal with skewed datasets, that over
represent items of certain classes, while underrepresenting the rest. Skewed
datasets may lead to unforeseen issues with models such as learning a biased
function or overfitting. Traditional data augmentation techniques in supervised
learning include oversampling and training with synthetic data. We introduce an
experimental approach to dealing with such unbalanced datasets by including
humans in the training process. We ask humans to rank the importance of
features of the dataset, and through rank aggregation, determine the initial
weight bias for the model. We show that collective human bias can allow ML
models to learn insights about the true population instead of the biased
sample. In this paper, we use two rank aggregator methods Kemeny Young and the
Markov Chain aggregator to quantify human opinion on importance of features.
This work mainly tests the effectiveness of human knowledge on binary
classification (Popular vs Not-popular) problems on two ML models: Deep Neural
Networks and Support Vector Machines. This approach considers humans as weak
learners and relies on aggregation to offset individual biases and domain
unfamiliarity
ARTEMIS: AI-driven Robotic Triage Labeling and Emergency Medical Information System
Mass casualty incidents (MCIs) pose a formidable challenge to emergency
medical services by overwhelming available resources and personnel. Effective
victim assessment is paramount to minimizing casualties during such a crisis.
In this paper, we introduce ARTEMIS, an AI-driven Robotic Triage Labeling and
Emergency Medical Information System. This system comprises a deep learning
model for acuity labeling that is integrated with a robot, that performs the
preliminary assessment of injury severity in patients and assigns appropriate
triage labels. Additionally, we have developed a frontend (graphical user
interface) that is updated by the robots in real time and is accessible to the
first responders. To validate the reliability of our proposed algorithmic
triage protocol, we employed an off-the-shelf robot kit equipped with sensors
for vital sign acquisition. A controlled laboratory simulation of an MCI was
conducted to assess the system's performance and effectiveness in real-world
scenarios resulting in a triage-level classification accuracy of 92%. This
noteworthy achievement underscores the model's proficiency in discerning
crucial patterns for accurate triage classification, showcasing its promising
potential in healthcare applications
Graph-based Decentralized Task Allocation for Multi-Robot Target Localization
We introduce a new approach to address the task allocation problem in a
system of heterogeneous robots comprising of Unmanned Ground Vehicles (UGVs)
and Unmanned Aerial Vehicles (UAVs). The proposed model, \texttt{\method}, or
\textbf{G}raph \textbf{A}ttention \textbf{T}ask \textbf{A}llocato\textbf{R}
aggregates information from neighbors in the multi-robot system, with the aim
of achieving joint optimality in the target localization efficiency.Being
decentralized, our method is highly robust and adaptable to situations where
collaborators may change over time, ensuring the continuity of the mission. We
also proposed heterogeneity-aware preprocessing to let all the different types
of robots collaborate with a uniform model.The experimental results demonstrate
the effectiveness and scalability of the proposed approach in a range of
simulated scenarios. The model can allocate targets' positions close to the
expert algorithm's result, with a median spatial gap less than a unit length.
This approach can be used in multi-robot systems deployed in search and rescue
missions, environmental monitoring, and disaster response
NIO: Lightweight neural operator-based architecture for video frame interpolation
We present, NIO - Neural Interpolation Operator, a lightweight efficient
neural operator-based architecture to perform video frame interpolation.
Current deep learning based methods rely on local convolutions for feature
learning and require a large amount of training on comprehensive datasets.
Furthermore, transformer-based architectures are large and need dedicated GPUs
for training. On the other hand, NIO, our neural operator-based approach learns
the features in the frames by translating the image matrix into the Fourier
space by using Fast Fourier Transform (FFT). The model performs global
convolution, making it discretization invariant. We show that NIO can produce
visually-smooth and accurate results and converges in fewer epochs than
state-of-the-art approaches. To evaluate the visual quality of our interpolated
frames, we calculate the structural similarity index (SSIM) and Peak Signal to
Noise Ratio (PSNR) between the generated frame and the ground truth frame. We
provide the quantitative performance of our model on Vimeo-90K dataset, DAVIS,
UCF101 and DISFA+ dataset
AffectEcho: Speaker Independent and Language-Agnostic Emotion and Affect Transfer for Speech Synthesis
Affect is an emotional characteristic encompassing valence, arousal, and
intensity, and is a crucial attribute for enabling authentic conversations.
While existing text-to-speech (TTS) and speech-to-speech systems rely on
strength embedding vectors and global style tokens to capture emotions, these
models represent emotions as a component of style or represent them in discrete
categories. We propose AffectEcho, an emotion translation model, that uses a
Vector Quantized codebook to model emotions within a quantized space featuring
five levels of affect intensity to capture complex nuances and subtle
differences in the same emotion. The quantized emotional embeddings are
implicitly derived from spoken speech samples, eliminating the need for one-hot
vectors or explicit strength embeddings. Experimental results demonstrate the
effectiveness of our approach in controlling the emotions of generated speech
while preserving identity, style, and emotional cadence unique to each speaker.
We showcase the language-independent emotion modeling capability of the
quantized emotional embeddings learned from a bilingual (English and Chinese)
speech corpus with an emotion transfer task from a reference speech to a target
speech. We achieve state-of-art results on both qualitative and quantitative
metrics
Neural Operator: Is data all you need to model the world? An insight into the impact of Physics Informed Machine Learning
Numerical approximations of partial differential equations (PDEs) are
routinely employed to formulate the solution of physics, engineering and
mathematical problems involving functions of several variables, such as the
propagation of heat or sound, fluid flow, elasticity, electrostatics,
electrodynamics, and more. While this has led to solving many complex
phenomena, there are some limitations. Conventional approaches such as Finite
Element Methods (FEMs) and Finite Differential Methods (FDMs) require
considerable time and are computationally expensive. In contrast, data driven
machine learning-based methods such as neural networks provide a faster, fairly
accurate alternative, and have certain advantages such as discretization
invariance and resolution invariance. This article aims to provide a
comprehensive insight into how data-driven approaches can complement
conventional techniques to solve engineering and physics problems, while also
noting some of the major pitfalls of machine learning-based approaches.
Furthermore, we highlight, a novel and fast machine learning-based approach
(~1000x) to learning the solution operator of a PDE operator learning. We will
note how these new computational approaches can bring immense advantages in
tackling many problems in fundamental and applied physics