Search Results for author:
Found 26 papers, 12 papers with code
GloCOM: A Short Text Neural Topic Model via Global Clustering Context
Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets.
Predicting from Strings: Language Model Embeddings for Bayesian Optimization
Bayesian Optimization is ubiquitous in the field of experimental design and blackbox optimization for improving search efficiency, but has been traditionally restricted to regression models which are only applicable to fixed search spaces and tabular input features.
NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization
Recent advances in neural topic models have concentrated on two primary directions: the integration of the inference network (encoder) with a pre-trained language model (PLM) and the modeling of the relationship between words and topics in the generative model (decoder).
ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution
Detecting and attributing temperature increases due to climate change is crucial for understanding global warming and guiding adaptation strategies.
LICO: Large Language Models for In-Context Molecular Optimization
However, directly prompting a pretrained language model to produce predictions is not feasible in many scientific domains due to the scarcity of domain-specific data in the pretraining corpora and the challenges of articulating complex problems in natural language.
Probing the Decision Boundaries of In-context Learning in Large Language Models
In-context learning is a key paradigm in large language models (LLMs) that enables them to generalize to new tasks and domains by simply prompting these models with a few exemplars without explicit parameter updates.
Rank-Preference Consistency as the Appropriate Metric for Recommender Systems
We propose what we consider to be a measure that is more fundamentally appropriate for assessing RS performance, rank-preference consistency, which simply counts the number of prediction pairs that are inconsistent with the user's expressed product preferences.
ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction
Thus, we propose ChaosBench, a challenging benchmark to extend the predictability range of data-driven weather emulators to S2S timescale.
A Study on Social Robot Behavior in Group Conversation
Recently, research in human-robot interaction began to consider a robot's influence at the group level.
Scaling transformer neural networks for skillful and reliable medium-range weather forecasting
At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals.
ExPT: Synthetic Pretraining for Few-Shot Experimental Design
In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available.
Imposing Consistency Properties on Blackbox Systems with Applications to SVD-Based Recommender Systems
In this paper we discuss pre- and post-processing methods to induce desired consistency and/or invariance properties in blackbox systems, e. g., AI-based.
An Admissible Shift-Consistent Method for Recommender Systems
In this paper, we propose a new constraint, called shift-consistency, for solving matrix/tensor completion problems in the context of recommender systems.
ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling
Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation and mitigation efforts.
Utilization of domain knowledge to improve POMDP belief estimation
The partially observable Markov decision process (POMDP) framework is a common approach for decision making under uncertainty.
ClimaX: A foundation model for weather and climate
We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings.
Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios
Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers.
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
We study CWBC in the context of RvS (Emmons et al., 2021) and Decision Transformers (Chen et al., 2021), and show that CWBC significantly boosts their performance on various benchmarks.
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling
We propose Transformer Neural Processes (TNPs), a new member of the NP family that casts uncertainty-aware meta learning as a sequence modeling problem.
A Simple and Scalable Tensor Completion Algorithm via Latent Invariant Constraint for Recommendation System
In this paper we provide a latent-variable formulation and solution to the recommender system (RS) problem in terms of a fundamental property that any reasonable solution should be expected to satisfy.
Tensor Completion with Provable Consistency and Fairness Guarantees for Recommender Systems
We introduce a new consistency-based approach for defining and solving nonnegative/positive matrix and tensor completion problems.
Temporal Predictive Coding For Model-Based Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
Non-Markovian Predictive Coding For Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments.
Predictive Coding for Locally-Linear Control
High-dimensional observations and unknown dynamics are major challenges when applying optimal control to many real-world decision making tasks.
DeepCABAC: A Universal Compression Algorithm for Deep Neural Networks
The field of video compression has developed some of the most sophisticated and efficient compression algorithms known in the literature, enabling very high compressibility for little loss of information.
Image Colorization Using a Deep Convolutional Neural Network
In this paper, we present a novel approach that uses deep learning techniques for colorizing grayscale images.
