Paper presents a robust transfer learning method for active level set estimation.
problem Efficiently identifying regions of a black-box function with limited function evaluations.
method Incorporates prior knowledge from a related function while locally adapting it.
result The method achieves better convergence of level sets compared to standard transfer learning.
The paper proposes a method to transfer knowledge across different settings using causal theory.
problem Learning transfer across similar but different settings.
method Bayesian perspective of causal theory induction, integrating instance-level associative learning and abstract-level structural causal knowledge.
result The proposed model achieved transfer behavior across trials and learning situations, unlike RL algorithms.
This paper improves ASR performance by aligning frames more accurately.
problem Disagreement between teacher-student models in frame-level alignment.
method Introduces self-knowledge distillation (SKD) to guide frame-level alignment.
result Improves both resource efficiency and performance in ASR.
Paper analyzes consistency between neural networks at different levels.
problem Understanding knowledge consistency between neural networks.
method Generic definition and task-agnostic method to disentangle consistent knowledge.
result Knowledge consistency provides new insights and can improve network performance.
CSEAL uses cognitive structure to personalize learning paths.
problem Personalized learning paths based on learners' evolving knowledge levels and item structures.
method CSEAL integrates knowledge levels and item structures using a Markov Decision Process and actor-critic algorithm.
result CSEAL effectively personalizes learning paths, improving learning outcomes.
OKDDip uses diverse peers to improve online knowledge distillation.
problem Early saturation in group-based distillation.
method Two-level distillation with multiple auxiliary peers and a group leader, using attention-based aggregation weights.
result OKDDip consistently gives better performance than state-of-the-art approaches.
HAKE embeds entities in polar coordinates to model semantic hierarchies in knowledge graphs.
problem Lack of modeling semantic hierarchies in knowledge graph embeddings.
method HAKE embeds entities in a polar coordinate system, where the radial coordinate represents hierarchy levels and the angular coordinate distinguishes entities at the same level.
result HAKE significantly outperforms existing methods on link prediction tasks in knowledge graphs.
Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent k…
New method learns from noisy data without knowing noise level.
problem Learning from noisy data without knowing noise level.
method Uses Stein's Unbiased Risk Estimate (SURE) without noise level knowledge.
result Outperforms other self-supervised methods on imaging problems.
TaRP predicts missing relations in KGs using type and instance-level info.
problem Missing relations in KGs.
method Type-augmented relation prediction (TaRP) combining type and instance-level info.
result Significantly better performance on benchmark datasets.
Deep learning based knowledge tracing model has been shown to outperform traditional knowledge tracing model without the need for human-engineered features, yet its parameters and representations have long been criticized for not being explainable. In this paper, we propose Deep-IRT which is a synthesis of the item res…
The paper characterizes the efficiency of transferring knowledge from a teacher to a student classifier over finite domains.
problem Characterizing the statistical efficiency of knowledge transfer over finite domains.
method Three progressive levels of privileged information: hard labels, teacher probabilities, and soft labels. Novel empirical loss functions used to achieve the fundamental limits.
result Achieving the fundamental limits of knowledge transfer through specific levels of privileged information and novel loss functions.
Add expert knowledge to resolve ambiguities in ANN models.
problem Non-unique parameter fitting in material science.
method Augment a black-box ANN model with expert knowledge at two levels.
result Expert knowledge resolves ambiguities in parameter space.
This study improves knowledge distillation for RNN-T models with noisy labels.
problem Challenges in distilling knowledge from RNN-T models with variable quality teachers.
method Full-sum distillation and sequence-level knowledge distillation.
result Full-sum distillation outperforms other methods for RNN-T models, especially for bad teachers.
Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, we propose a few-shot learning method for wearable sensor based human activity recognition, a technique that seeks high-level human activity knowledge from lo…
Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.
problem Symbolic knowledge in neural models can make models more susceptible to adversarial attacks.
method Investigated deep probabilistic graphical models that incorporate symbolic knowledge and neural nets.
result Symbolic knowledge can propagate the negative effects of adversarial examples, making models more vulnerable.
Proposes a deep learning framework guided by human advice.
problem Learning from sparse and noisy data.
method Knowledge-augmented Column Networks, leveraging human advice.
result Improves model performance in domains with structured representations.
A new personality-based recommender system tackles data sparsity without feedback.
problem Data sparsity without common feedback among users.
method Implicitly identifying users' personality type and incorporating it with personal interests and knowledge level.
result The model's effectiveness, especially in data sparsity situations, demonstrated on a real-world dataset.
Improved sentiment analysis in Korean finance using masked PLMs.
problem Fine-grained sentiment analysis in non-English finance literature is lacking due to limited annotated data.
method Developed KorFinASC dataset and applied TGT-Masking to PLMs to remove non-stationary knowledge.
result Improved classification accuracy by 22.63% on KorFinASC.
In this paper, we consider advancing web-scale knowledge extraction and alignment by integrating OpenIE extractions in the form of (subject, predicate, object) triples with Knowledge Bases (KB). Traditional techniques from universal schema and from schema mapping fall in two extremes: either they perform instance-level…
Complex systems may contain heterogeneous types of variables that interact in a multi-level and multi-scale manner. In this context, high-level layers may considered as groups of variables interacting in lower-level layers. This is particularly true in biology, where, for example, genes are grouped in pathways and two …
We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extra…
YouTube presents an unprecedented opportunity to explore how machine learning methods can improve healthcare information dissemination. We propose an interdisciplinary lens that synthesizes machine learning methods with healthcare informatics themes to address the critical issue of developing a scalable algorithmic sol…
Graph representation learning improves with domain knowledge.
problem Efficiently learning graph representations from scarce labels.
method Multi-task knowledge distillation combining graph metrics.
result Improves prediction performance, especially with limited training data.
ZSL-KG learns class representations from common sense knowledge graphs.
problem Predicting classes without labeled examples using semantic class representations.
method TrGCN, a novel transformer graph convolutional network, embeds nodes from common sense knowledge graphs in a vector space.
result ZSL-KG improves over existing methods on five out of six zero-shot benchmark datasets.
Paper proposes a method to generate instance labels from weakly supervised data.
problem Weakly supervised instance labeling in medical image analysis.
method Uses multiple instance learning (MIL) and knowledge distillation to generate instance-level predictions.
result Significantly outperforms state-of-the-art MIL methods in instance-level prediction.
Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…
Survey of integrating physics knowledge into machine learning models.
problem Mitigating data shortage and ensuring physical plausibility.
method Combining physics knowledge with machine learning models.
result Summarizes recent works in physics-informed machine learning.
New approach for multi-task reinforcement learning without task interference.
problem Efficient knowledge sharing between tasks in reinforcement learning.
method Attention-based multi-task deep reinforcement learning.
result Achieves positive knowledge transfer and avoids negative transfer.
RID framework quantifies and regularizes task-relevant knowledge in distillation.
problem Distilling irrelevant information can hinder student model performance.
method Partial Information Decomposition to quantify and regularize task-relevant knowledge.
result RID framework leads to more resilient distillation under nuisance teachers.
ProbKT uses probabilistic logical reasoning to train object detection models with weak supervision.
problem Training object detection models requires instance-level annotations, which are often unavailable.
method ProbKT, a framework based on probabilistic logical reasoning, uses arbitrary types of weak supervision.
result ProbKT leads to significant improvement and better generalization compared to existing baselines.
Incorporation of a new knowledge into neural networks with simultaneous preservation of the previous one is known to be a nontrivial problem. This problem becomes even more complex when new knowledge is contained not in new training examples, but inside the parameters (connection weights) of another neural network. Her…
To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on lar…
Transfer learning improves algorithm recommendation performance.
problem Improving algorithm recommendation with limited data.
method Train a neural network on meta-datasets, then transfer knowledge to similar datasets.
result Transfer learning enhances meta-learning for algorithm recommendation.
In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some kind of higher-level symbolic inference. Therefore, there is a clear need for the definition of a general and tight integration between low-le…
This research debiases machine unlearning by using counterfactual examples.
problem Machine unlearning processes can be biased, leading to inaccurate results.
method Intervention-based approach using counterfactual examples to mitigate biases.
result The method outperforms existing baselines on evaluation metrics.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
problem Understanding the dynamics of drug pipelines in global pharmaceutical industry.
method Multilayer network analysis of drug pipeline, global supply chain, and ownership data.
result Proven similarities in knowledge flows between pharmaceutical and AI industries.
This paper improves understanding of knowledge distillation and its effects.
problem Improving model quality with a fixed capacity budget.
method Categorizing teacher's knowledge into three levels and studying their effects on distillation.
result The three hierarchical levels of teacher's knowledge (universe, domain, instance) significantly impact knowledge distillation.
Bayesian Dark Knowledge fails to perform well with high posterior uncertainty.
problem Performance degradation of Bayesian Dark Knowledge with high posterior uncertainty.
method Compresses posterior predictive distribution into a single network, using a student network matching the teacher ensemble architecture.
result Using a matching student network architecture does not guarantee acceptable performance with high posterior uncertainty.
CLEAS improves neural architecture search for continual learning.
problem Overcoming catastrophic forgetting and adapting to new tasks while controlling model complexity.
method Neural architecture search (NAS) with reinforcement learning to find optimal neural architecture.
result CLEAS achieves higher classification accuracy with simpler neural architectures.
Bayesian model transfers knowledge across different engineering fleets.
problem Data sparsity in predictive models for engineering infrastructure.
method Hierarchical Bayesian approach with multitask learning.
result Improves survival analysis and power prediction in truck fleets and wind farms.
Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform. By tracking the evolution of the knowledge of some student, one can optimize instruction. Existing methods are either based on temporal latent variabl…
System helps engineers with concept recognition for SEVA.
problem Recognizing systems engineering concepts for SEVA.
method Token classification task, domain expert labeling, pre-trained model fine-tuning, essential datasets creation, knowledge graph construction.
result System successfully recognizes systems engineering concepts.
Fastest video anomaly detection via teacher-student distillation.
problem Anomaly detection in video at high speed.
method Adversarial knowledge distillation from object-level teacher models.
result 7-62 times faster than state-of-the-art methods.
Previous transfer learning methods based on deep network assume the knowledge should be transferred between the same hidden layers of the source domain and the target domains. This assumption doesn't always hold true, especially when the data from the two domains are heterogeneous with different resolutions. In such ca…
GMVAEs cluster and generate game levels without labels.
problem Manual exploration of latent space for VAE-generated game levels.
method Apply Gaussian Mixture VAEs (GMVAEs) to learn latent clusters and generate levels.
result GMVAEs can cluster and generate game levels without requiring labels.
Deep networks, composed of multiple layers of hierarchical distributed representations, tend to learn low-level features in initial layers and transition to high-level features towards final layers. Paradigms such as transfer learning, multi-task learning, and continual learning leverage this notion of generic hierarch…
The paper improves NLI models' robustness by adding external knowledge to the attention mechanism.
problem NLI models' performance drops significantly on simple adversarial examples.
method Proposes a method to enhance NLI models' robustness by incorporating external knowledge into the attention mechanism.
result The method significantly improves the robustness of NLI models, achieving human-level performance on adversarial data.