Model ranks abstract anaphors based on their relation to antecedents.
problem Resolving abstract anaphora in text understanding.
method LSTM-Siamese Net learns mention-ranking through artificial data.
result Model outperforms state-of-the-art on shell noun resolution.
New corpus improves coreference resolution by removing gender and number cues.
problem Challenges in resolving ambiguous pronoun references.
method Developed a new annotated corpus, introduced antecedent switching technique.
result Models perform poorly on ambiguous pronoun references, but antecedent switching improves performance.
Paper uses weakly-supervised clustering to automatically create network protocol abstractions.
problem Manual definition of abstraction by domain experts is time-consuming.
method Weakly supervised clustering algorithm for automatic abstraction.
result The method successfully matches the reference abstraction with minimal labeled examples.
Abstracts index for ML4H workshop at NeurIPS 2019.
problem No specific problem stated; index of accepted abstracts.
method Not specified; index of accepted abstracts.
result No specific result stated.
The paper aims to mathematically define and learn abstractions from data.
problem Defining and learning abstractions from data.
method Characterize abstractions as summaries for answering queries, define leakiness as a loss function, and generalize classical statistics.
result A mathematical theory of abstraction can be learned from data.
Unified framework for causal models at different levels of abstraction.
problem Relating causal models at varying levels of abstraction.
method Categorical framework using natural transformations between Markov functors.
result Generalized and unified causal abstractions with categorical proofs.
Abstract MDPs enable strategic exploration and fast reward transfer in complex environments.
problem Challenging to learn accurate MDPs for high-dimensional states.
method Learn an abstract MDP over low-dimensional coarse states, using an abstraction function.
result Achieves superhuman performance on Pitfall! and higher reward with fewer samples.
Large dataset for medical abstracts classified by sentence role.
problem Efficiently classify long medical abstracts for researchers.
method Labeled dataset of 200k abstracts, each with 2.3M sentences.
result Improved sentence classification for medical literature.
PALM learns abstract models for efficient planning and task transfer.
problem Efficiently learning and transferring hierarchical models for planning.
method PALM uses a new formal structure (L-AMDP) to learn independent, modular models at multiple levels of abstraction.
result PALM integrates planning and execution, facilitating rapid learning of abstract models.
Tabular Q-Learning with learned state abstractions solves continuous control tasks.
problem Challenging reinforcement learning problems in continuous control.
method Learned state abstraction to transform continuous state-space into discrete.
result Tabular Q-Learning with learned abstractions achieves efficient learning in unseen tasks.
Study investigates how simple speech sounds can form abstract categories.
problem How do abstract categories like phonemes emerge from speech exposure?
method Used modeling techniques to test Memory-Based Learning and Error-Correction Learning.
result Error-Correction Learning models can learn abstractions, identifying phone inventory and grouping.
New approach to abstract neural network representations using renormalization group.
problem Developing truly abstract representations in neural networks.
method Renormalization group approach to expand representations to encompass broader data sets.
result Representations in neural networks become more abstract as data breadth increases and depth increases.
Abstract Neural Networks (ANNs) improve DNN verification efficiency.
problem Efficiently verify safety-critical DNNs without slowing exponentially.
method Introduces ANNs that use abstract domains and activation functions to overapproximate DNNs.
result ANNs can soundly overapproximate DNNs with fewer nodes, improving verification efficiency.
Algorithm finds latent structure in value functions for improved reinforcement learning.
problem Finding latent structure in value functions for efficient reinforcement learning.
method Proposes a practical algorithm using two posterior distributions over state abstractions and abstract-state values.
result Substantial performance gains in multi-task settings where tasks share a common, low-dimensional representation.
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artifici…
This paper uses group theory to create data-free, feature-free clustering.
problem Creating data-free, feature-free hierarchical clustering.
method Symmetry-driven hierarchical clustering using group theory.
result A new clustering framework that is globally hierarchical and data-free.
New algorithm learns abstractions from experience to simplify complex tasks.
problem Solving complex problems in environments with continuous state spaces.
method Uses MDP homomorphisms to find abstract MDPs and guides exploration.
result Demonstrates task transfer method outperforms deep Q-networks.
Proves embedding theorem for definable manifolds.
problem Embedding abstract-definable Cp manifolds into Euclidean space. method Proves Whitney embedding theorem for definable manifolds.
result Abstract-definable Cp manifolds are Cp embedded into RN. Abstract operator calculus solves fermionic quantum harmonic oscillator problems.
problem Eigenvalue problems of fermionic quantum harmonic oscillators.
method Abstract operator calculus using homotopy operator.
result Formulated eigenvalue problem resembling fermionic quantum harmonic oscillator.
The paper proposes a principle for dynamically adjusting the granularity of reinforcement learning abstractions.
problem Lack of general principles for dynamically adjusting the granularity of reinforcement learning abstractions.
method The paper proposes a principle based on rate-distortion theory, formalized through a performance certificate decomposing value error into learning and abstraction error bounds.
result Soft state-action abstractions can achieve near-optimal performance under substantial lossy compression of state and action information.
Develops a framework for decision-making abstractions under computational limitations.
problem Decision-making by agents with limited computational resources.
method Information-theoretic signal compression and optimization problem formulation.
result Generates a hierarchy of abstractions for a non-trivial environment.
Abstract: Extends theorems about statistical structures to abstract manifolds with curvature bounds.
problem Generalizing theorems about statistical structures to abstract manifolds with curvature bounds.
method Extends theorems about statistical structures to abstract manifolds with curvature bounds.
result Theorems about statistical structures on abstract manifolds with curvature bounds are generalized.
New system defends deep neural networks like ResNet-34.
problem Training deep neural networks like ResNet-34 is challenging.
method Differentiable abstract interpretation and a DSL for training objectives.
result Can defend significantly larger networks than before.
This paper simplifies OPE in large state spaces using state abstractions.
problem Accurately evaluating policies offline in large state spaces.
method Developed a backward-model-irrelevance condition and an iterative state abstraction procedure.
result Deeply-abstracted states substantially simplify OPE sample complexity.
Abstract Syntax Networks generate code and parse text with high accuracy.
problem Mapping unstructured inputs to well-formed outputs for code generation and semantic parsing.
method Dynamic AST construction using a decoder with a modular structure.
result 79.2 BLEU and 22.7% exact match accuracy on Hearthstone dataset.
CIB compresses variables causally, preserving key causal interactions.
problem Constructing causal variable abstractions in complex systems.
method Causal Information Bottleneck (CIB) method, extending IB to include causal structures.
result CIB produces causally interpretable abstractions that accurately capture causal relations.
Constellation learns group-level visual relationships for abstract reasoning.
problem Learning configurational properties of entire groups of objects.
method Introduces Constellation, a network that learns relational abstractions over static visual scenes.
result Offers a basis for abstract relational reasoning and sensory imagination.
Deep neural network learns discrete state abstractions for efficient planning.
problem Efficient sequential decision making in large state spaces.
method Information bottleneck method for learning approximate bisimulations using deep neural encoders and action-conditioned HMM.
result Trained method efficiently plans for unseen goals in multi-goal reinforcement learning.
Abstract discusses different corks.
problem None specified in abstract.
method None specified in abstract.
result None specified in abstract.
moment maps arise as a generalization of genuine moment maps on symplectic manifolds when the symplectic structure is discarded, but the relation between the mapping and the action is kept. Particular examples of abstract moment maps had been used in Hamiltonian mechanics for some time, but the abstract notion originat…
ARNe model excels in abstract visual reasoning tasks.
problem Abstract visual reasoning using attention mechanisms.
method Hybrid network architecture combining self-attention and relational reasoning.
result ARNe model surpasses WReN model by 11.28 ppt on PGM datasets.
Defines a generalized string concept for abstract root systems.
problem Generalizing the concept of strings to abstract root systems.
method Introduces a new definition for Φ-strings in abstract root systems. result Defines a new set of elements in Σ based on a given λ and subset Φ of simple roots. Generative models use latent abstractions to create images.
problem Understanding how generative models create high-dimensional data like images.
method Developed a theoretical framework using SDE and information theory.
result Diffusion models can be seen as a non-linear filter driven by latent abstractions.
NHC learns scalable algorithmic solutions from diverse tasks.
problem Neural networks struggle to learn algorithmic strategies.
method Memory-augmented network with abstraction mechanism and evolutionary training.
result Reliable learning of robust and scalable algorithmic solutions.
Math theory explains how neural networks learn abstract representations.
problem Understanding how neural networks learn abstract representations.
method Mathematical theory reformulating network optimization into mean field optimization over neural preactivations.
result Abstract representations of latent variables are guaranteed to appear in neural networks trained on tasks that depend on these variables.
PLOT uses optimal transport to find neural site handles for causal abstraction.
problem Finding the relevant neural site for causal analysis is computationally challenging.
method PLOT employs optimal transport to localize causal variables from neural network outputs.
result PLOT efficiently finds intervention handles for causal abstraction in neural networks.
Automatically verifies robustness of SVMs against adversarial examples.
problem Ensuring SVMs are robust to adversarial attacks.
method Abstract verification using interval domain and reduced affine forms.
result Encouragingly high percentages of provable robustness on MNIST test set.
Proposes method to learn state abstractions that generalize across environments.
problem Learning abstractions that generalize in block MDPs.
method Invariant causal prediction to learn model-irrelevant state abstractions (MISA).
result Proves high probability of outputting a state abstraction corresponding to causal feature set for return.
Minimal learning agents can infer unobserved variables in complex environments.
problem How to infer unobserved variables in complex environments using minimal learning agents.
method Concrete operational definition of abstract concepts, minimal architecture supporting abstraction, reinforcement learning.
result Minimal learning agents can infer the existence of unobserved variables.
In this paper, we prove the infinite dimensionality of some local and global cohomology groups on abstract Cauchy-Riemann manifolds.
Abstracts connections in tangent categories, proving classical results.
problem Defining and understanding connections in abstract tangent categories.
method Investigates connections in tangent categories, deriving classical results.
result Derives classical results about connections in tangent categories.
Abstract Morse index theorem applied to various optimization problems.
problem Optimization problems with constraints in Hilbert spaces.
method Abstract Morse index theorem in Hilbert space.
result Precise changes in index and nullity when restricting to subspaces.
Disentangled representations improve abstract visual reasoning tasks.
problem The usefulness of disentangled representations for abstract visual reasoning.
method A large-scale study with 360 state-of-the-art unsupervised disentanglement models and 3600 abstract reasoning models.
result Disentangled representations lead to better down-stream performance in abstract reasoning tasks.
STAR framework reduces OPE variance by distilling complex problems into discrete ARPs.
problem High variance and bias in off-policy evaluation methods.
method STAR framework that includes various OPE estimators and leverages state abstraction.
result Predictions from ARPs estimated from off-policy data are asymptotically correct.
EncGAN learns multi-manifold structure and abstract features using an encoder.
problem Learning multi-manifold structure and abstract features in data.
method Uses an encoder to model manifold structure and invert it for generation, with a single latent space for shared abstract features.
result Successfully learns multi-manifold structure and abstract features on MNIST, 3D-chair, and UT-Zap50k datasets.
Proposes deep multimodal fusion for biometric identification.
problem Improving biometric identification accuracy with multiple modalities.
method Joint optimization of multiple modality-specific CNNs at different feature abstraction levels.
result Significant improvement in multimodal person identification performance.
Predicts whether an image is an abstraction of text or vice versa.
problem Understanding semantic cross-modal relations between images and text.
method Introduces a new metric (ABS) and a deep learning autoencoder approach to predict it.
result Demonstrates feasibility of predicting the relative abstractness level of image-text pairs.
Scalable verifier for recurrent neural networks using polyhedral abstractions.
problem Certifying the correctness of recurrent neural networks.
method Combining sampling, optimization, and Fermat's theorem for polyhedral abstractions; gradient descent for refinement.
result Successfully verified challenging recurrent models in various domains.