Search Results for author:
Found 28 papers, 16 papers with code
Hypothesis Generation with Large Language Models
We focus on hypothesis generation based on data (i. e., labeled examples).
MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?"
WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
We present WeaverBird, an intelligent dialogue system designed specifically for the finance domain.
EasyTPP: Towards Open Benchmarking Temporal Point Processes
In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.
Statler: State-Maintaining Language Models for Embodied Reasoning
In this paper, we explore a new dimension in which large language models may benefit robotics planning.
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Large language models have shown astonishing performance on a wide range of reasoning tasks.
Autoregressive Modeling with Lookahead Attention
To predict the next token, autoregressive models ordinarily examine the past.
Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions
Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users.
Explicit Planning Helps Language Models in Logical Reasoning
In this paper, we propose LEAP, a novel system that uses language models to perform multi-step logical reasoning and incorporates explicit planning into the inference procedure.
Robustness of Learning from Task Instructions
To our knowledge, this is the first work that systematically studies how robust a PLM is when it is supervised by instructions with different factors of variability.
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning
We propose to select layers based on the variability of their hidden states given a task-specific corpus.
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Our tiny-attention adapter learns to modify the hidden states at each position directly conditioned on the hidden states at all the other positions, which is missed by the previously proposed adapters.
HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
In this paper, we tackle the important yet under-investigated problem of making long-horizon prediction of event sequences.
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized.
Transformer Embeddings of Irregularly Spaced Events and Their Participants
The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced sequences of discrete events.
Noise-Contrastive Estimation for Multivariate Point Processes
The log-likelihood of a generative model often involves both positive and negative terms.
Personalized Dynamic Treatment Regimes in Continuous Time: A Bayesian Approach for Optimizing Clinical Decisions with Timing
In the second step, we propose a policy gradient method to learn the personalized optimal clinical decision that maximizes the patient survival by interacting the MTPP with the model on clinical observations while accounting for uncertainties in clinical observations learned from the posterior inference of the Bayesian joint model in the first step.
Methodology
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification
Learning how to predict future events from patterns of past events is difficult when the set of possible event types is large.
Informed Temporal Modeling via Logical Specification of Factorial LSTMs
Consider a world in which events occur that involve various entities.
Imputing Missing Events in Continuous-Time Event Streams
On held-out incomplete sequences, our method is effective at inferring the ground-truth unobserved events, with particle smoothing consistently improving upon particle filtering.
On the Idiosyncrasies of the Mandarin Chinese Classifier System
While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods.
Inference of unobserved event streams with neural Hawkes particle smoothing
Particle smoothing is an extension of particle filtering in which proposed events are conditioned on the future as well as the past.
Halo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction
Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios.
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process
Many events occur in the world.
Coherent Dialogue with Attention-based Language Models
We model coherent conversation continuation via RNN-based dialogue models equipped with a dynamic attention mechanism.
Accurate Vision-based Vehicle Localization using Satellite Imagery
We propose a method for accurately localizing ground vehicles with the aid of satellite imagery.
What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment
We propose an end-to-end, domain-independent neural encoder-aligner-decoder model for selective generation, i. e., the joint task of content selection and surface realization.
Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences
We propose a neural sequence-to-sequence model for direction following, a task that is essential to realizing effective autonomous agents.
