520 research outputs found
Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics
Traditional unsupervised topic modeling approaches like Latent Dirichlet Allocation (LDA) lack the ability to classify documents into a predefined set of topics. On the other hand, supervised methods require significant amounts of labeled data to perform well on such tasks. We develop a new unsupervised method based on word embeddings to classify documents into predefined topics. We evaluate the predictive performance of this novel approach and compare it to seeded LDA. We use a real-world dataset from online advertising, which is comprised of markedly short documents. Our results indicate the two methods may complement one another well, leading to remarkable sensitivity and precision scores of ensemble learners trained thereupon
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
Unsupervised text encoding models have recently fueled substantial progress
in NLP. The key idea is to use neural networks to convert words in texts to
vector space representations based on word positions in a sentence and their
contexts, which are suitable for end-to-end training of downstream tasks. We
see a strikingly similar situation in spatial analysis, which focuses on
incorporating both absolute positions and spatial contexts of geographic
objects such as POIs into models. A general-purpose representation model for
space is valuable for a multitude of tasks. However, no such general model
exists to date beyond simply applying discretization or feed-forward nets to
coordinates, and little effort has been put into jointly modeling distributions
with vastly different characteristics, which commonly emerges from GIS data.
Meanwhile, Nobel Prize-winning Neuroscience research shows that grid cells in
mammals provide a multi-scale periodic representation that functions as a
metric for location encoding and is critical for recognizing places and for
path-integration. Therefore, we propose a representation learning model called
Space2Vec to encode the absolute positions and spatial relationships of places.
We conduct experiments on two real-world geographic data for two different
tasks: 1) predicting types of POIs given their positions and context, 2) image
classification leveraging their geo-locations. Results show that because of its
multi-scale representations, Space2Vec outperforms well-established ML
approaches such as RBF kernels, multi-layer feed-forward nets, and tile
embedding approaches for location modeling and image classification tasks.
Detailed analysis shows that all baselines can at most well handle distribution
at one scale but show poor performances in other scales. In contrast,
Space2Vec's multi-scale representation can handle distributions at different
scales.Comment: 15 pages; Accepted to ICLR 2020 as a spotlight pape
Understanding stock market instability via graph auto-encoders
Understanding stock market instability is a key question in financial
management as practitioners seek to forecast breakdowns in asset co-movements
which expose portfolios to rapid and devastating collapses in value. The
structure of these co-movements can be described as a graph where companies are
represented by nodes and edges capture correlations between their price
movements. Learning a timely indicator of co-movement breakdowns (manifested as
modifications in the graph structure) is central in understanding both
financial stability and volatility forecasting. We propose to use the edge
reconstruction accuracy of a graph auto-encoder (GAE) as an indicator for how
spatially homogeneous connections between assets are, which, based on financial
network literature, we use as a proxy to infer market volatility. Our
experiments on the S&P 500 over the 2015-2022 period show that higher GAE
reconstruction error values are correlated with higher volatility. We also show
that out-of-sample autoregressive modeling of volatility is improved by the
addition of the proposed measure. Our paper contributes to the literature of
machine learning in finance particularly in the context of understanding stock
market instability.Comment: Submitted to Glinda workshop of the Neurips 2022 conference Keywords
: Graph Based Learning, Graph Neural Networks, Graph Autoencoder, Stock
Market Information, Volatility Forecastin
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Large language models (LLMs) have significantly advanced the field of natural
language processing (NLP), providing a highly useful, task-agnostic foundation
for a wide range of applications. However, directly applying LLMs to solve
sophisticated problems in specific domains meets many hurdles, caused by the
heterogeneity of domain data, the sophistication of domain knowledge, the
uniqueness of domain objectives, and the diversity of the constraints (e.g.,
various social norms, cultural conformity, religious beliefs, and ethical
standards in the domain applications). Domain specification techniques are key
to make large language models disruptive in many applications. Specifically, to
solve these hurdles, there has been a notable increase in research and
practices conducted in recent years on the domain specialization of LLMs. This
emerging field of study, with its substantial potential for impact,
necessitates a comprehensive and systematic review to better summarize and
guide ongoing work in this area. In this article, we present a comprehensive
survey on domain specification techniques for large language models, an
emerging direction critical for large language model applications. First, we
propose a systematic taxonomy that categorizes the LLM domain-specialization
techniques based on the accessibility to LLMs and summarizes the framework for
all the subcategories as well as their relations and differences to each other.
Second, we present an extensive taxonomy of critical application domains that
can benefit dramatically from specialized LLMs, discussing their practical
significance and open challenges. Last, we offer our insights into the current
research status and future trends in this area
Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation
Prior work on Sign Language Translation has shown that having a mid-level
sign gloss representation (effectively recognizing the individual signs)
improves the translation performance drastically. In fact, the current
state-of-the-art in translation requires gloss level tokenization in order to
work. We introduce a novel transformer based architecture that jointly learns
Continuous Sign Language Recognition and Translation while being trainable in
an end-to-end manner. This is achieved by using a Connectionist Temporal
Classification (CTC) loss to bind the recognition and translation problems into
a single unified architecture. This joint approach does not require any
ground-truth timing information, simultaneously solving two co-dependant
sequence-to-sequence learning problems and leads to significant performance
gains.
We evaluate the recognition and translation performances of our approaches on
the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset. We report
state-of-the-art sign language recognition and translation results achieved by
our Sign Language Transformers. Our translation networks outperform both sign
video to spoken language and gloss to spoken language translation models, in
some cases more than doubling the performance (9.58 vs. 21.80 BLEU-4 Score). We
also share new baseline translation results using transformer networks for
several other text-to-text sign language translation tasks
CoMFormer: Continual Learning in Semantic and Panoptic Segmentation
Continual learning for segmentation has recently seen increasing interest.
However, all previous works focus on narrow semantic segmentation and disregard
panoptic segmentation, an important task with real-world impacts. %a In this
paper, we present the first continual learning model capable of operating on
both semantic and panoptic segmentation. Inspired by recent transformer
approaches that consider segmentation as a mask-classification problem, we
design CoMFormer. Our method carefully exploits the properties of transformer
architectures to learn new classes over time. Specifically, we propose a novel
adaptive distillation loss along with a mask-based pseudo-labeling technique to
effectively prevent forgetting. To evaluate our approach, we introduce a novel
continual panoptic segmentation benchmark on the challenging ADE20K dataset.
Our CoMFormer outperforms all the existing baselines by forgetting less old
classes but also learning more effectively new classes. In addition, we also
report an extensive evaluation in the large-scale continual semantic
segmentation scenario showing that CoMFormer also significantly outperforms
state-of-the-art methods.Comment: Under submissio
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional
sparse graph-structured data into low-dimensional dense vectors, which is a
fundamental task that has been widely studied in a range of fields, including
machine learning and data mining. Classic graph embedding methods follow the
basic idea that the embedding vectors of interconnected nodes in the graph can
still maintain a relatively close distance, thereby preserving the structural
information between the nodes in the graph. However, this is sub-optimal due
to: (i) traditional methods have limited model capacity which limits the
learning performance; (ii) existing techniques typically rely on unsupervised
learning strategies and fail to couple with the latest learning paradigms;
(iii) representation learning and downstream tasks are dependent on each other
which should be jointly enhanced. With the remarkable success of deep learning,
deep graph representation learning has shown great potential and advantages
over shallow (traditional) methods, there exist a large number of deep graph
representation learning techniques have been proposed in the past decade,
especially graph neural networks. In this survey, we conduct a comprehensive
survey on current deep graph representation learning algorithms by proposing a
new taxonomy of existing state-of-the-art literature. Specifically, we
systematically summarize the essential components of graph representation
learning and categorize existing approaches by the ways of graph neural network
architectures and the most recent advanced learning paradigms. Moreover, this
survey also provides the practical and promising applications of deep graph
representation learning. Last but not least, we state new perspectives and
suggest challenging directions which deserve further investigations in the
future
Actively learning a Bayesian matrix fusion model with deep side information
High-dimensional deep neural network representations of images and concepts
can be aligned to predict human annotations of diverse stimuli. However, such
alignment requires the costly collection of behavioral responses, such that, in
practice, the deep-feature spaces are only ever sparsely sampled. Here, we
propose an active learning approach to adaptively sampling experimental stimuli
to efficiently learn a Bayesian matrix factorization model with deep side
information. We observe a significant efficiency gain over a passive baseline.
Furthermore, with a sequential batched sampling strategy, the algorithm is
applicable not only to small datasets collected from traditional laboratory
experiments but also to settings where large-scale crowdsourced data collection
is needed to accurately align the high-dimensional deep feature representations
derived from pre-trained networks
- …