42 research outputs found
Constraining Implicit Space with Minimum Description Length: An Unsupervised Attention Mechanism across Neural Network Layers
Inspired by the adaptation phenomenon of neuronal firing, we propose the
regularity normalization (RN) as an unsupervised attention mechanism (UAM)
which computes the statistical regularity in the implicit space of neural
networks under the Minimum Description Length (MDL) principle. Treating the
neural network optimization process as a partially observable model selection
problem, UAM constrains the implicit space by a normalization factor, the
universal code length. We compute this universal code incrementally across
neural network layers and demonstrated the flexibility to include data priors
such as top-down attention and other oracle information. Empirically, our
approach outperforms existing normalization methods in tackling limited,
imbalanced and non-stationary input distribution in image classification,
classic control, procedurally-generated reinforcement learning, generative
modeling, handwriting generation and question answering tasks with various
neural network architectures. Lastly, UAM tracks dependency and critical
learning stages across layers and recurrent time steps of deep networks
Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook
In recent years, reinforcement learning and bandits have transformed a wide
range of real-world applications including healthcare, finance, recommendation
systems, robotics, and last but not least, the speech and natural language
processing. While most speech and language applications of reinforcement
learning algorithms are centered around improving the training of deep neural
networks with its flexible optimization properties, there are still many
grounds to explore to utilize the benefits of reinforcement learning, such as
its reward-driven adaptability, state representations, temporal structures and
generalizability. In this survey, we present an overview of recent advancements
of reinforcement learning and bandits, and discuss how they can be effectively
employed to solve speech and natural language processing problems with models
that are adaptive, interactive and scalable.Comment: To appear in Expert Systems with Applications. Accompanying
INTERSPEECH 2022 Tutorial on the same topic. Including latest advancements in
large language models (LLMs
The Topology and Geometry of Neural Representations
A central question for neuroscience is how to characterize brain
representations of perceptual and cognitive content. An ideal characterization
should distinguish different functional regions with robustness to noise and
idiosyncrasies of individual brains that do not correspond to computational
differences. Previous studies have characterized brain representations by their
representational geometry, which is defined by the representational
dissimilarity matrix (RDM), a summary statistic that abstracts from the roles
of individual neurons (or responses channels) and characterizes the
discriminability of stimuli. Here we explore a further step of abstraction:
from the geometry to the topology of brain representations. We propose
topological representational similarity analysis (tRSA), an extension of
representational similarity analysis (RSA) that uses a family of
geo-topological summary statistics that generalizes the RDM to characterize the
topology while de-emphasizing the geometry. We evaluate this new family of
statistics in terms of the sensitivity and specificity for model selection
using both simulations and functional MRI (fMRI) data. In the simulations, the
ground truth is a data-generating layer representation in a neural network
model and the models are the same and other layers in different model instances
(trained from different random seeds). In fMRI, the ground truth is a visual
area and the models are the same and other areas measured in different
subjects. Results show that topology-sensitive characterizations of population
codes are robust to noise and interindividual variability and maintain
excellent sensitivity to the unique representational signatures of different
neural network layers and brain regions.Comment: codes: https://github.com/doerlbh/TopologicalRS
Working Alliance Transformer for Psychotherapy Dialogue Classification
As a predictive measure of the treatment outcome in psychotherapy, the
working alliance measures the agreement of the patient and the therapist in
terms of their bond, task and goal. Long been a clinical quantity estimated by
the patients' and therapists' self-evaluative reports, we believe that the
working alliance can be better characterized using natural language processing
technique directly in the dialogue transcribed in each therapy session. In this
work, we propose the Working Alliance Transformer (WAT), a Transformer-based
classification model that has a psychological state encoder which infers the
working alliance scores by projecting the embedding of the dialogues turns onto
the embedding space of the clinical inventory for working alliance. We evaluate
our method in a real-world dataset with over 950 therapy sessions with anxiety,
depression, schizophrenia and suicidal patients and demonstrate an empirical
advantage of using information about the therapeutic states in this sequence
classification task of psychotherapy dialogues