Search Results for author:
Found 21 papers, 10 papers with code
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image
3D reconstruction from single view images is an ill-posed problem.
CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets.
Machine Translation: A Literature Review
Machine translation (MT) plays an important role in benefiting linguists, sociologists, computer scientists, etc.
Probabilistic Federated Neural Matching
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
Bayesian Nonparametric Federated Learning of Neural Networks
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
A Bandit Approach to Posterior Dialog Orchestration Under a Budget
Building multi-domain AI agents is a challenging task and an open problem in the area of AI.
Statistical Model Aggregation via Parameter Matching
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
Project CLAI: Instrumenting the Command Line as a New Environment for AI Agents
This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI).
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume.
Online Semi-Supervised Learning with Bandit Feedback
We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits, motivated by several applications including clini-cal trials and ad recommendations.
NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands
The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.
COVID-19 India Dataset: Parsing COVID-19 Data in Daily Health Bulletins from States in India
While India has been one of the hotspots of COVID-19, data about the pandemic from the country has proved to be largely inaccessible at scale.
Using Document Similarity Methods to create Parallel Datasets for Code Translation
However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages.
On sensitivity of meta-learning to support data
Meta-learning algorithms are widely used for few-shot learning.
Investigating Explainability of Generative AI for Code through Scenario-based Design
Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion.
Geometry-biased Transformers for Novel View Synthesis
While this representation yields (coarsely) accurate images corresponding to novel viewpoints, the lack of geometric reasoning limits the quality of these outputs.
Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness
While several toolkits exist to aid machine learning practitioners in addressing fairness issues, little is known about the strategies practitioners employ to evaluate model fairness and what factors influence their assessment, particularly in the context of text classification.
An Investigation of Representation and Allocation Harms in Contrastive Learning
In this paper, we demonstrate that contrastive learning (CL), a popular variant of SSL, tends to collapse representations of minority groups with certain majority groups.
Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations
This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models.
Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models
In this work, we explore data-efficient adaptation of pre-trained code models by further pre-training and fine-tuning them with program structures.
Aligners: Decoupling LLMs and Alignment
Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications.
