The research team at Abacus.AI makes fundamental contributions to the field of AI/ML with wide
ranging impact to both foundational and applied topics for the betterment of our customers and science.
We focus on a wide range of topics, including large language models, automated machine learning,
meta-learning, data augmentation, deep learning optimization, and fairness/debiasing in deep learning.
The team has an impressive list of publications in top-tier conferences.
Read on for detailed explanations of our active areas of research and product development, which
are focused on solving hard problems faced by today's organizations.
We also are committed to open-source Generative AI and have several open-source contributions.
Large Language Models and Foundation Models
Large Language Models - At Abacus.AI, we actively research how large language models can be adapted for client’s needs,
particularly for in-demand industry applications such as chat bots, search, and question answering. We offer these solutions
by starting with state-of-the-art large language models, and then performing a custom fine-tuning procedure in order to
maximize the accuracy of the desired task. We are able to adapt LLMs to customer needs by learning from
their knowledge base of internal documents, customer feedback, code, etc. and deploying solutions into production.
We are deeply committed to open-source research in LLMs and have worked actively on collaboratively building solutions in the open.
One challenge that the community faces is cheaply building LLMs which can read and reason about longer input texts.
This concept of context extension is a core area of research for Abacus.AI and one where we’ve proven advanced techniques
in both algorithmically increasing context length and expansive datasets to measure progress.
Code Generation - In recent years, researchers have made remarkable advances in generative language modeling tasks,
with present generative AI systems capable of writing syntactically and functionally correct code.
With the increased capabilities of generative AI systems, it is natural to ask questions about the ability of generative systems
to write executable code by data scientists and organizations. We are researching the correct ways to build and deploy these systems,
with a particular focus on how to evaluate code generating systems.
At Abacus.AI, we believe that the next frontier for language models is in AI-assisted data science.
We are developing generative models that can interpret and then execute commands given in natural language
(such as, “Train a 10-layer MLP and plot the validation accuracy over time”).
By fine-tuning the model on our own Abacus.AI API’s, the language model allows anyone to easily use Abacus’ platform
to perform exploratory data analysis, visualize data, and train powerful machine learning models.
Time-Series Forecasting - Time-series forecasting is a ubiquitous problem in the industry, used for problems such as
forecasting inventory demand and predicting stock prices. While statistical approaches such as ARIMA and Prophet are widely used,
deep learning models such as transformers are getting increasingly more popular. However, many high-impact forecasting problems are
either low-data, or low signal-to-noise ratio, and many forecastable datasets have standard attributes that carry over to other datasets.
With this intuition in mind, Abacus.AI has developed a radically new approach to time-series forecasting.
Inspired by recent innovations in meta-learning and Bayesian inference on tabular data, we have designed
the world’s first foundation model for time-series forecasting. To achieve this, we pretrain a transformer
on a mix of real-world and synthetic datasets, across a variety of different forecasting tasks, resulting
in a state-of-the-art forecasting model that can run inference on a new dataset in less than a second.
Samuel Dooley, Gurnoor Singh Khurana, Chirag Mohapatra, Siddartha Venkat Naidu, Colin White
NeurIPS 2023
Deep recommender systems
Deep Recommender Systems - Recommender systems are used heavily across e-commerce,
social
media, and entertainment companies such as Amazon, YouTube, and Netflix. While early
approaches
included k-nearest-neighbor or matrix factorization, deep learning is becoming the dominant
paradigm in recommender systems due to the scale and complexity of modern recommender system
datasets. At Abacus.AI, we have developed multiple deep learning approaches to recommender
systems
for use cases such as session-based item recommendation, personalized search query results,
and
personalized related item recommendations. Our deep learning models are based on
state-of-the-art
techniques such as two-tower models, which use separate embedding layers for the user
history as
well as the items, and then makes recommendations based on cosine similarity. We also use
recurrent neural networks such as long short-term memory (LSTM) to capture temporal
relationships
in each user’s history.
Meta-Learning to Ensure Performance - In order to ensure our deep learning models
achieve the highest possible performance, we use meta-learning to rapidly find the best
algorithm and hyperparameters for new datasets. For more details, see our published research
paper below. Our system also compares the best approaches to well-tuned baselines such as
P3Alpha, RP3Beta, SLIM-Elastic-Net, and Co-Clustering, to make sure that we achieve the best
possible performance.
Vector Store - The size of the datasets in large retail and e-commerce
companies can reach millions of items and billions of total interactions, and deployed
models often face millions of requests per day. In order to keep the latency of each request
low, Abacus.AI uses our custom-designed, state-of-the-art vector store. Powered by
GPUs, our vector store is able to scale up to 10,000+ queries per second, with
less than 20ms latency per request.
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, Colin White
Workshop at AutoML-Conf 2022
Training with Less Data
While organizations today might have large amounts of data, their datasets tend to be noisy,
incomplete and imbalanced. This results in data scientists and engineers spending most of their
precious time pre-processing, cleaning, and featurizing the data. These efforts are often
insufficient, and deep learning techniques routinely fail on sparse datasets. Organizations are
then forced to use classical machine learning techniques that require enormous amounts of manual
feature engineering. At Abacus.AI, we are actively pursuing the following
research areas that will enable training on less data.
Meta-Learning - Deep learning models typically require training with a large number
of training samples. On the other hand, humans learn concepts and skills much more quickly
and efficiently. We only need a few examples to tell lions apart from cats. Meta-learning is
a sub-field of machine learning that aims to teach models how to learn. We hope to build on
the work outlined by Model-Agnostic Meta-Learning (MAML) and first-order
Meta-Learning Algorithms. The MAML algorithm provides a good initialization of a model’s
parameters to achieve an optimal fast learning on a new task with only a small number of
gradient steps while avoiding overfitting that may happen when using a small dataset. Our
service uses principles of meta-learning to create robust models even when you have a small
number of training examples.
Generative Models for Dataset Augmentation - Dataset augmentation is a technique to
synthetically expand a training dataset by applying a wide array of domain-specific
transformations. This is a particularly useful tool for small datasets, and it is even shown
to be effective on
large datasets like Imagenet. While it is a standard tool used in training supervised
deep learning models, it requires extensive domain knowledge, and the transformations must
be designed and tested carefully. Over the last 2 years, Generative Adversarial Networks
(GANs) have been used successfully for dataset augmentation in various domains including computer
vision, anomaly
detection, and forecasting.
The use of GANs makes dataset augmentation possible even with little or no domain-specific
knowledge. Fundamentally, GANs learn how to produce data from a dataset that is
indistinguishable from the original data. However, there are some practical issues with
using GANs, and training a GAN is notoriously difficult. GANs have
been a very active area of research, and several new types of GANs
including Wasserstein
GANs and MMD
GANs address some of these issues. Recently, there has also been some work on domain-agnostic
GAN implementation for dataset augmentation. At Abacus.AI, we are
innovating on the state-of-the-art GAN algorithms that can perform well on noisy and
incomplete datasets. We have innovated on Data Augmentation Generative Adversarial Networks
to create synthetic datasets that can be combined with original datasets to create more
robust models. The demo on our homepage is based on GANs. Check out this blog post to see how it works.
Combining Neural Nets with Logic Rules/Specifications - The cognitive process of
human beings indicates that people learn not only from concrete examples (as deep neural
nets do) but also from different forms of general knowledge and rich experiences. It’s
difficult to encode human intention to guide the models to capture desired patterns. In
fact, most enterprise systems today are rule-based. Experts have encoded rules based on
tribal knowledge from their domains. ML models that are built to replace these rule-based
systems often struggle to beat them on accuracy, especially when there is sparse data.
At Abacus.AI, we are working on preserving expert knowledge by
developing hybrid systems that combine logic rules with
neural nets. While there is some recent research in this area, including a recent paper by
DeepMind that lays the groundwork for a general-purpose, constraint-driven AI, it is
still nascent. Most research papers don’t address building these hybrid models at scale or
incorporating multiple rules into the models. Abacus.AI is working on
a service that allows developers and data scientists to specify multiple knowledge rules
along with training data to develop accurate models. For example, there may be a rule that
‘dog owners tend to like buying dog toys’ in a recommender system or a constraint that a
learned dynamic system must be consistent with physical law. Our publication in this area combines first-order logic constraints with
conventional supervised learning.
Transfer Learning - Transfer learning is a machine learning technique that allows us
to reuse policies from one domain or dataset on a related domain or dataset. By using
transfer learning, we enable organizations to train models in a simulated environment and
apply them in the real world. State-of-the-art language and vision modeling techniques
typically pre-train on a large dataset, then either use fine-tuning or transfer learning to
train a custom model on the target dataset. Abacus.AI packages and
extends the state-of-the-art transfer learning techniques that result in the most
performant models. As part of our service, we plan to package pre-trained language and
vision models. We’ll also make it easy to fine-tune those models or apply transfer learning
to adapt them for a custom task.
NeurIPS Workshop on Knowledge Representation to ML, 2019
AI-Assisted ML
Deep learning has seen great success across a wide variety of domains. The best neural architectures
are often carefully constructed by seasoned deep learning experts in each domain. For example, years
of experimentation have shown how to arrange bidirectional transformers to work well for language
tasks and dilated separable convolutions for image tasks. A relatively new sub-field of
deep-learning deals with automated machine learning, or as we prefer to call it: AI-assisted machine
learning. The fundamental idea is that AI will create a first pass of the deep-learning model given
a use-case or a dataset. Developers/data scientists can then either use that model directly or
fine-tune. We are conducting cutting-edge research in the main pillars of AI-Assisted ML:
hyperparameter optimization (HPO) and neural architecture search (NAS).
Hyperparameter optimization
When developing a deep learning model, there are many knobs and dials to tune that
depend on the specific task and dataset at hand. For example, setting the learning rate
too high can prevent the algorithm from converging. Setting the learning rate too low
can cause the algorithm to get stuck at a local minimum. There are countless other
hyperparameters such as the number of epochs, batch size, momentum, regularization,
shape, and size of the neural network. These hyperparameters are all dependent on each
other and interact in intricate ways, so finding the best hyperparameters for a given
dataset is an extremely difficult and highly nonconvex optimization problem.
Randomly testing different sets of hyperparameters may eventually find a decent solution
but could take years of computation time. Efficiently tuning deep learning
hyperparameters is an active area of research. Five years ago, the best algorithms
weren’t much better than random search. Now algorithms are capable of orders of
magnitude speedups. At Abacus.AI, we use state-of-the-art HPO
while training all our models.
Neural Architecture Search
Neural architecture search (NAS) is a rapidly developing area of research in which the
process of choosing the best architecture is automated.
At Abacus.AI, we are using NAS to both fine-tune proven deep
network paradigms, and learn novel architectures for new domains. Our goal is to empower
data scientists and developers to create custom, production-grade models in days, not
months. See this blog post to read about our method, BANANAS,
which combines Bayesian optimization with neural predictors to
achieve state-of-the-art performance. Since making our code open-source, dozens of
developers have forked our repository, and two
independentresearch groups have confirmed that it achieves
state-of-the-art performance on NAS-Bench-101. BANANAS has even been cited in survey
papers on NAS.
We are also actively conducting fundamental research on the theory of NAS. Recently, we
studied local search for NAS - a simple yet effective
approach. We showed experimentally that local search gives state-of-the-art performance
on smaller benchmark NAS search spaces, but performs worse than random search on
extremely large search spaces. Motivated by this stark contrast, we gave a complete
theoretical characterization of local search. Our theoretical results confirm that local
search performs well on smaller search spaces and when the search space exhibits
locality.
Finally, we are conducting formal studies on the building blocks of NAS, including the
architecture
encoding. In most NAS algorithms, the neural architectures must be passed as
input to the algorithm using some encoding. For example, we might encode the neural
architectures using an adjacency matrix. Our recent work shows that this encoding can
have a substantial impact on the final result of the NAS algorithm. We conduct a set of
experiments with eight different encodings with various NAS algorithms. Our results lay
out recommendations for the best encodings to use in different settings within NAS.
Colin White, Arber Zela, Binxin Ru, Yang Liu, Frank Hutter
Selected as a contributed talk | ICLR Workshop on Neural Architecture Search
Bias and Explainability in Neural Nets
Bias is one of the most important issues in machine learning today. Deep learning models are
being deployed in high-stakes scenarios today more than ever, and most of these models are found
to exhibit prejudices. For example, the New York Times reported that the majority of facial
recognition apps used by law enforcement agencies exhibit bias. They cited a study concluding
that facial recognition technology is ten times more likely to falsely identify people of color,
women and older people.
There has been considerable research in mitigating these biases, with dozens of definitions of bias and algorithms to decrease the level of
bias. The majority of fair algorithms are in-processing algorithms, which take as input a
training dataset and then train a new, fairer model from scratch. However, this is not always
practical. For example, recent neural networks such as XLNet or GPT-3 can take
weeks to train and are very expensive. Additionally, for some applications,
the full training set may no longer be available due to regulatory or privacy requirements.
At Abacus.AI, we are designing new post-hoc methods, which take as input a
pretrained model and a smaller validation dataset, and then debias the model through fine-tuning
or post-processing. We have designed three new techniques which work for applications with
tabular data or structured data. See our blog post for more information.
In addition to bias, we are actively working on explainability in neural networks. Business
Analysts and subject matter experts within organizations are often frustrated when dealing with
deep learning models. These models can appear to be black boxes that generate predictions which
humans can’t explain. Over the last two years, there has been considerable research in
explainability in AI. This has resulted in the release of an open-source tool, LIME,
which measures the responsiveness of a model’s outputs to perturbations in its inputs. Then
there’s SHAP (SHapley Additive exPlanations), a game-theoretic
approach to explain the output of any machine learning model. Google has introduced Testing with
Concept Activation Vectors (TCAV), a technique that may be used to generate insights and
hypotheses. Google Brain’s scientists also explored attribution of predictions to input features
in their 2016 paper, Axiomatic
attribution for deep neural networks. Our efforts in this area build on these techniques to
create a cloud microservice that will explain model predictions and determine if models exhibit
bias.
Forecasting and Planning
Anomaly Detection
Language AI
Personalization AI
Marketing and Sales AI
Foundation Models
Vision AI
Fraud and Security