Search Results for author:
Found 34 papers, 21 papers with code
Unified Training of Universal Time Series Forecasting Transformers
Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models.
Moonshot: Towards Controllable Video Generation and Editing with Multimodal Conditions
This work presents Moonshot, a new video generation model that conditions simultaneously on multimodal inputs of image and text.
CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules
We find that by naturally encouraging the LLM to reuse the previously developed and verified sub-modules, CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests.
Ranked #2 on
Code Generation
on CodeContests
(Test Set pass@1 metric)
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
Time series has been left behind in the era of pre-training and transfer learning.
PyRCA: A Library for Metric-based Root Cause Analysis
We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps).
OTW: Optimal Transport Warping for Time Series
Dynamic Time Warping (DTW) has become the pragmatic choice for measuring distance between time series.
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges
There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency.
LogAI: A Library for Log Analytics and Intelligence
In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github. com/salesforce/logai), a one-stop open source library for log analytics and intelligence.
Learning Deep Time-index Models for Time Series Forecasting
Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models.
Learning Fast and Slow for Online Time Series Forecasting
The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting.
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Transformers have been actively studied for time-series forecasting in recent years.
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective.
Merlion: A Machine Learning Library for Time Series
We introduce Merlion, an open-source machine learning library for time series.
PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection
Despite achieving promising performance at par with anchor-based detectors, the existing anchor-free detectors such as FCOS or CenterNet predict objects based on standard Cartesian coordinates, which often yield poor quality keypoints.
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior
Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.
Contextual Transformation Networks for Online Continual Learning
Continual learning methods with fixed architectures rely on a single network to learn models that can perform well on all tasks.
BiST: Bi-directional Spatio-Temporal Reasoning for Video-Grounded Dialogues
Video-grounded dialogues are very challenging due to (i) the complexity of videos which contain both spatial and temporal variations, and (ii) the complexity of user utterances which query different segments and/or different objects in videos over multiple dialogue turns.
Bilevel Continual Learning
Continual learning aims to learn continuously from a stream of tasks and data in an online-learning fashion, being capable of exploiting what was learned previously to improve current and future tasks while still being able to perform well on the previous tasks.
Adaptive Task Sampling for Meta-Learning
Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks.
UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues
Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons.
Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism
Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc.
Ranked #7 on
Cross-Modal Retrieval
on Recipe1M
FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging
FoodAI has made food logging convenient, aiding smart consumption and a healthy lifestyle.
Meta-RCNN: Meta Learning for Few-Shot Object Detection
Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data.
Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior
Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.
Recent Advances in Deep Learning for Object Detection
Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.
Multimodal Transformer Networks for End-to-End Video-Grounded Dialogue Systems
Developing Video-Grounded Dialogue Systems (VGDS), where a dialogue is conducted based on visual and audio aspects of a given video, is significantly more challenging than traditional image or text-grounded dialogue systems because (1) feature space of videos span across multiple picture frames, making it difficult to obtain semantic information; and (2) a dialogue agent must perceive and process information from different modalities (audio, video, caption, etc.)
Ranked #4 on
Response Generation
on SIMMC2.0
Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images
Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle.
Ranked #8 on
Cross-Modal Retrieval
on Recipe1M
Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift
Most existing work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labeled data is available in the training tasks.
URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection
This approach allows the model to capture several types of semantic information, which was not possible by the existing models.
Online Learning: A Comprehensive Survey
Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instances one at a time.
Online Deep Learning: Learning Deep Neural Networks on the Fly
Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task.
Malicious URL Detection using Machine Learning: A Survey
This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning.
SOL: A Library for Scalable Online Learning Algorithms
SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data.
Budget Online Multiple Kernel Learning
To overcome this drawback, we present a novel framework of Budget Online Multiple Kernel Learning (BOMKL) and propose a new Sparse Passive Aggressive learning to perform effective budget online learning.
