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Found 66 papers, 24 papers with code
WatClaimCheck: A new Dataset for Claim Entailment and Inference
We contribute a new dataset for the task of automated fact checking and an evaluation of state of the art algorithms.
Online Bayesian Moment Matching based SAT Solver Heuristics
Our solvers out-perform the baselines by solving 12 more instances from the SAT competition 2018 application benchmark and are %40 faster on average in solving hard cryptographic instances.
Continuation KD: Improved Knowledge Distillation through the Lens of Continuation Optimization
Knowledge Distillation (KD) has been extensively used for natural language understanding (NLU) tasks to improve a small model's (a student) generalization by transferring the knowledge from a larger model (a teacher).
Label Alignment Regularization for Distribution Shift
Recent work reported the label alignment property in a supervised learning setting: the vector of all labels in the dataset is mostly in the span of the top few singular vectors of the data matrix.
Learning Functions on Multiple Sets using Multi-Set Transformers
We propose a general deep architecture for learning functions on multiple permutation-invariant sets.
Benchmarking Constraint Inference in Inverse Reinforcement Learning
When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints.
Robust One Round Federated Learning with Predictive Space Bayesian Inference
In contrast, our method performs the aggregation on the predictive posteriors, which are typically easier to approximate owing to the low-dimensionality of the output space.
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
We present Federated Bayesian Neural Regression (FedBNR), an algorithm that learns a scalable stand-alone global federated GP that respects clients' privacy.
Learning Soft Constraints From Constrained Expert Demonstrations
We consider the setting where the reward function is given, and the constraints are unknown, and propose a method that is able to recover these constraints satisfactorily from the expert data.
FedFormer: Contextual Federation with Attention in Reinforcement Learning
We evaluate our methods on the Meta-World environment and find that our approach yields significant improvements over FedAvg and non-federated Soft Actor-Critic single-agent methods.
Do we need Label Regularization to Fine-tune Pre-trained Language Models?
Knowledge Distillation (KD) is a prominent neural model compression technique that heavily relies on teacher network predictions to guide the training of a student model.
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge Distillation
Knowledge distillation (KD) is an efficient framework for compressing large-scale pre-trained language models.
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks
Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks.
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning
We propose a Represent And Mimic (RAMi) framework for training 1) an identifiable latent representation to capture the independent factors of variation for the objects and 2) a mimic tree that extracts the causal impact of the latent features on DRL action values.
Distributional Reinforcement Learning with Monotonic Splines
Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs.
Distributional Reinforcement Learning
reinforcement-learning
+1
Learning Object-Oriented Dynamics for Planning from Text
The advancement of dynamics models enables model-based planning in complex environments.
RAIL-KD: RAndom Intermediate Layer Mapping for Knowledge Distillation
To address these problems, we propose a RAndom Intermediate Layer Knowledge Distillation (RAIL-KD) approach in which, intermediate layers from the teacher model are selected randomly to be distilled into the intermediate layers of the student model.
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and instantaneous (intra-slice) relations among variables.
Quantifying and Improving Transferability in Domain Generalization
We then prove that our transferability can be estimated with enough samples and give a new upper bound for the target error based on our transferability.
Robust Embeddings Via Distributions
Despite recent monumental advances in the field, many Natural Language Processing (NLP) models still struggle to perform adequately on noisy domains.
Self-Supervised Simultaneous Multi-Step Prediction of Road Dynamics and Cost Map
While supervised learning is widely used for perception modules in conventional autonomous driving solutions, scalability is hindered by the huge amount of data labeling needed.
Partially Observable Mean Field Reinforcement Learning
Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents.
Multi-agent Reinforcement Learning
Q-Learning
Multiagent Systems
Prediction by Anticipation: An Action-Conditional Prediction Method based on Interaction Learning
In autonomous driving (AD), accurately predicting changes in the environment can effectively improve safety and comfort.
Learning Agent Representations for Ice Hockey
This paper develops a new approach for agent representations, based on a Markov game model, that is tailored towards applications in professional ice hockey.
Newton-type Methods for Minimax Optimization
We prove their local convergence at strict local minimax points, which are surrogates of global solutions.
A Positivstellensatz for Conditional SAGE Signomials
Recently, the conditional SAGE certificate has been proposed as a sufficient condition for signomial positivity over a convex set.
Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory
State-of-the-art neural dialogue systems excel at syntactic and semantic modelling of language, but often have a hard time establishing emotional alignment with the human interactant during a conversation.
Optimality and Stability in Non-Convex Smooth Games
Convergence to a saddle point for convex-concave functions has been studied for decades, while recent years has seen a surge of interest in non-convex (zero-sum) smooth games, motivated by their recent wide applications.
Batch norm with entropic regularization turns deterministic autoencoders into generative models
The variational autoencoder is a well defined deep generative model that utilizes an encoder-decoder framework where an encoding neural network outputs a non-deterministic code for reconstructing an input.
Learning Dynamic Belief Graphs to Generalize on Text-Based Games
Playing text-based games requires skills in processing natural language and sequential decision making.
Multi Type Mean Field Reinforcement Learning
We consider two different kinds of mean field environments: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations.
Unsupervised Multilingual Alignment using Wasserstein Barycenter
We study unsupervised multilingual alignment, the problem of finding word-to-word translations between multiple languages without using any parallel data.
Ranked #1 on
Word Alignment
on en-es
MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection
We present MatrixNets (xNets), a new deep architecture for object detection.
Matrix Nets: A New Deep Architecture for Object Detection
We present Matrix Nets (xNets), a new deep architecture for object detection.
Ranked #110 on
Object Detection
on COCO test-dev
Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously.
Comparing EM with GD in Mixture Models of Two Components
In the case of mixtures of Bernoullis, we find that there exist one-cluster regions that are stable for GD and therefore trap GD, but those regions are unstable for EM, allowing EM to escape.
Diachronic Embedding for Temporal Knowledge Graph Completion
In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.
Representation Learning for Dynamic Graphs: A Survey
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance.
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks
We introduce SPFlow, an open-source Python library providing a simple interface to inference, learning and manipulation routines for deep and tractable probabilistic models called Sum-Product Networks (SPNs).
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
At their core, many unsupervised learning models provide a compact representation of homogeneous density mixtures, but their similarities and differences are not always clearly understood.
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable.
Monte-Carlo Tree Search for Constrained POMDPs
In this paper, we present CC-POMCP (Cost-Constrained POMCP), an online MCTS algorithm for large CPOMDPs that leverages the optimization of LP-induced parameters and only requires a black-box simulator of the environment.
Progressive Memory Banks for Incremental Domain Adaptation
The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity.
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
The detection of moving objects is done in an unsupervised way by exploiting structure from motion.
On Improving Deep Reinforcement Learning for POMDPs
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e. g., computer Go.
Variational Attention for Sequence-to-Sequence Models
The variational encoder-decoder (VED) encodes source information as a set of random variables using a neural network, which in turn is decoded into target data using another neural network.
Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation
This paper addresses the question: Why do neural dialog systems generate short and meaningless replies?
Affective Neural Response Generation
Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content.
Order-Planning Neural Text Generation From Structured Data
Generating texts from structured data (e. g., a table) is important for various natural language processing tasks such as question answering and dialog systems.
On Improving Deep Reinforcement Learning for POMDPs
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e. g., computer Go.
Generative Mixture of Networks
The model consists of K networks that are trained together to learn the underlying distribution of a given data set.
Online Structure Learning for Sum-Product Networks with Gaussian Leaves
Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable.
Deep Active Learning for Dialogue Generation
We propose an online, end-to-end, neural generative conversational model for open-domain dialogue.
Discovering Conversational Dependencies between Messages in Dialogs
We investigate the task of inferring conversational dependencies between messages in one-on-one online chat, which has become one of the most popular forms of customer service.
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
When the number of topics (or latent groups) is unknown, the Hierarchical Dirichlet Process (HDP) provides an elegant non-parametric extension; however, it is a complex model and it is difficult to incorporate prior knowledge since the distribution over topics is implicit.
Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching
The Gaussian mixture model is a classic technique for clustering and data modeling that is used in numerous applications.
A Unified Approach for Learning the Parameters of Sum-Product Networks
We present a unified approach for learning the parameters of Sum-Product networks (SPNs).
Dynamic Sum Product Networks for Tractable Inference on Sequence Data (Extended Version)
Since SPNs represent distributions over a fixed set of variables only, we propose dynamic sum product networks (DSPNs) as a generalization of SPNs for sequence data of varying length.
Self-Adaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e. g., word-phrase-sentence) plays a central role in natural language processing.
Ranked #5 on
Subjectivity Analysis
on SUBJ
On the Relationship between Sum-Product Networks and Bayesian Networks
We conclude the paper with some discussion of the implications of the proof and establish a connection between the depth of an SPN and a lower bound of the tree-width of its corresponding BN.
A Sober Look at Spectral Learning
In contrast, maximum likelihood estimates may get trapped in local optima due to the non-convex nature of the likelihood function of latent variable models.
Exploiting Structure in Weighted Model Counting Approaches to Probabilistic Inference
Previous studies have demonstrated that encoding a Bayesian network into a SAT formula and then performing weighted model counting using a backtracking search algorithm can be an effective method for exact inference.
Symbolic Dynamic Programming for Continuous State and Observation POMDPs
In recent years, point- based value iteration methods have proven to be extremely effective techniques for finding (approximately) optimal dynamic programming solutions to POMDPs when an initial set of belief states is known.
Cost-Sensitive Exploration in Bayesian Reinforcement Learning
In this paper, we consider Bayesian reinforcement learning (BRL) where actions incur costs in addition to rewards, and thus exploration has to be constrained in terms of the expected total cost while learning to maximize the expected long-term total reward.
Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints
We demonstrate that consistency with an expert's test selection leads to non-convex constraints on the model parameters.
