Search Results for author:
Found 28 papers, 7 papers with code
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
We present an auditing framework that offers a holistic assessment of synthetic datasets and AI models trained on them, centered around bias and discrimination prevention, fidelity to the real data, utility, robustness, and privacy preservation.
Knowledge Graph Generation From Text
In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages.
Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling
We introduce Reprogramming for Protein Sequence Infilling, a framework in which pretrained natural language models are repurposed for protein sequence infilling via reprogramming, to infill protein sequence templates as a method of novel protein generation.
AlphaFold Distillation for Improved Inverse Protein Folding
In this work, we propose to perform knowledge distillation on the folding model's confidence metrics, e. g., pTM or pLDDT scores, to obtain a smaller, faster and end-to-end differentiable distilled model, which then can be included as part of the structure consistency regularized inverse folding model training.
Cloud-Based Real-Time Molecular Screening Platform with MolFormer
With the prospect of automating a number of chemical tasks with high fidelity, chemical language processing models are emerging at a rapid speed.
Benchmarking deep generative models for diverse antibody sequence design
Here we consider three recently proposed deep generative frameworks for protein design: (AR) the sequence-based autoregressive generative model, (GVP) the precise structure-based graph neural network, and Fold2Seq that leverages a fuzzy and scale-free representation of a three-dimensional fold, while enforcing structure-to-sequence (and vice versa) consistency.
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models
Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning.
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.
Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
Image captioning has recently demonstrated impressive progress largely owing to the introduction of neural network algorithms trained on curated dataset like MS-COCO.
Alleviating Noisy Data in Image Captioning with Cooperative Distillation
Image captioning systems have made substantial progress, largely due to the availability of curated datasets like Microsoft COCO or Vizwiz that have accurate descriptions of their corresponding images.
Tabular Transformers for Modeling Multivariate Time Series
This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.
DualTKB: A Dual Learning Bridge between Text and Knowledge Base
In this work, we present a dual learning approach for unsupervised text to path and path to text transfers in Commonsense Knowledge Bases (KBs).
Optimizing Mode Connectivity via Neuron Alignment
Yet, current curve finding algorithms do not consider the influence of symmetry in the loss surface created by model weight permutations.
Optimizing Loss Landscape Connectivity via Neuron Alignment
Empirically, this initialization is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes.
Estimating Information Flow in DNNs
We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models.
Improved Adversarial Image Captioning
In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions.
Wasserstein Barycenter Model Ensembling
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.
Estimating Information Flow in Deep Neural Networks
We then develop a rigorous estimator for $I(X;T)$ in noisy DNNs and observe compression in various models.
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer
We introduce a new approach to tackle the problem of offensive language in online social media.
Adversarial Semantic Alignment for Improved Image Captions
When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training.
Deep learning algorithm for data-driven simulation of noisy dynamical system
It is shown that, when the numerical discretization is used, the function estimation problem can be solved by a multi-label classification problem.
Learning temporal evolution of probability distribution with Recurrent Neural Network
We propose to tackle a time series regression problem by computing temporal evolution of a probability density function to provide a probabilistic forecast.
Improved Neural Text Attribute Transfer with Non-parallel Data
Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes.
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting
In many multivariate time series modeling problems, there is usually a significant linear dependency component, for which VARs are suitable, and a nonlinear component, for which RNNs are suitable.
SenGen: Sentence Generating Neural Variational Topic Model
We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence.
Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems
In this context the goal is to detect anomalous flight segments, due to mechanical, environmental, or human factors in order to identifying operationally significant events and provide insights into the flight operations and highlight otherwise unavailable potential safety risks and precursors to accidents.
Estimating Structured Vector Autoregressive Model
While considerable advances have been made in estimating high-dimensional structured models from independent data using Lasso-type models, limited progress has been made for settings when the samples are dependent.
A Spectral Algorithm for Inference in Hidden Semi-Markov Models
Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs).
