Suzuki, Taiji
- Diffusion Models are Minimax Optimal Distribution Estimators
2023/03/03 by Kazusato Oko, Oko, Kazusato, Shunta Akiyama +3 · 31 citations
Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Radiomics and Machine Learning in Medical Imaging #Statistical Methods and Inference
- Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
2018/10/18 by Taiji Suzuki, Suzuki, Taiji · 21 citations
Computer Science · Earth and Planetary Sciences · #Advanced Data Compression Techniques #FOS: Computer and information sciences #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Seismic Imaging and Inversion Techniques
- High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation
2022/05/03 by Jimmy Ba, Murat A. Erdogdu, Ba, Jimmy +9 · 20 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Neural Networks and Applications #Statistics Theory (math.ST) #Stochastic Gradient Optimization Techniques
- Relative Density-Ratio Estimation for Robust Distribution Comparison
2011/06/23 by Makoto Yamada, Yamada, Makoto, Taiji Suzuki +7 · 9 citations
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification #Methodology (stat.ME) #Statistics Theory (math.ST)
- Compression based bound for non-compressed network: unified generalization error analysis of large compressible deep neural network
2019/09/25 by Suzuki, Taiji, Abe, Hiroshi, Nishimura, Tomoaki · 7 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Convex Analysis of the Mean Field Langevin Dynamics
2022/01/25 by Atsushi Nitanda, Denny Wu, Nitanda, Atsushi +3 · 9 citations
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Stochastic Gradient Optimization Techniques
- Spectral norm of random tensors
2014/07/07 by Tomioka, Ryota, Suzuki, Taiji · 5 citations
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Statistics Theory (math.ST)
- Mechanistic Design and Scaling of Hybrid Architectures
2024/03/26 by Michael Poli, Poli, Michael, Armin W. Thomas +21 · 15 citations
Engineering · #Architecture and Computational Design
- Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
2019/10/28 by Taiji Suzuki, Atsushi Nitanda, Suzuki, Taiji +1 · 8 citations
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical Approximation and Integration #Medical Image Segmentation Techniques #Sparse and Compressive Sensing Techniques
- Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
2020/06/15 by Oono, Kenta, Suzuki, Taiji · 6 citations
#05C99 #62M45 #FOS: Computer and information sciences #FOS: Mathematics #G.2.2 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistics Theory (math.ST)
- Stochastic Particle Gradient Descent for Infinite Ensembles
2017/12/14 by Nitanda, Atsushi, Suzuki, Taiji · 5 citations
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
- Transformers Provably Solve Parity Efficiently with Chain of Thought
2024/10/11 by Kim, Juno, Suzuki, Taiji · 16 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- When Does Preconditioning Help or Hurt Generalization?
2020/06/18 by Шун-ичи Амари, Jimmy Ba, Amari, Shun-ichi +13 · 6 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques
- Pretrained transformer efficiently learns low-dimensional target functions in-context
2024/11/04 by Kazusato Oko, Oko, Kazusato, Yujin Song +5 · 14 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
2024/06/03 by Jason D. Lee, Lee, Jason D., Kazusato Oko +5 · 10 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Transformers are Minimax Optimal Nonparametric In-Context Learners
2024/08/22 by Juno Kim, Tai Nakamaki, Kim, Juno +3 · 11 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Approximation and Estimation Ability of Transformers for Sequence-to-Sequence Functions with Infinite Dimensional Input
2023/05/30 by Shokichi Takakura, Takakura, Shokichi, Taiji Suzuki +1 · 6 citations
Computer Science · Medicine · #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Radiomics and Machine Learning in Medical Imaging
- Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape
2024/02/02 by Juno Kim, Kim, Juno, Taiji Suzuki +1 · 9 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems
2019/05/23 by Atsushi Nitanda, Geoffrey Chinot, Nitanda, Atsushi +3 · 7 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Neural Networks and Applications #Stochastic Gradient Optimization Techniques
- Flow matching achieves almost minimax optimal convergence
2024/05/31 by Kenji Fukumizu, Taiji Suzuki, Fukumizu, Kenji +7 · 7 citations
Engineering · Computer Science · #Advanced Control Systems Optimization #Reinforcement Learning in Robotics
- Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime
2020/06/22 by Atsushi Nitanda, Taiji Suzuki, Nitanda, Atsushi +1 · 3 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Model Reduction and Neural Networks #Stochastic Gradient Optimization Techniques
- Gradient-Based Feature Learning under Structured Data
2023/09/07 by Alireza Mousavi-Hosseini, Denny Wu, Mousavi-Hosseini, Alireza +5 · 6 citations
Computer Science · Environmental Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning
2021/02/05 by Tomoya Murata, Taiji Suzuki, Murata, Tomoya +1 · 3 citations
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Privacy-Preserving Technologies in Data #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
- On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
2021/06/11 by Shunta Akiyama, Akiyama, Shunta, Taiji Suzuki +1 · 3 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
- How do Transformers perform In-Context Autoregressive Learning?
2024/02/08 by Michael E. Sander, Sander, Michael E., Raja Giryes +7 · 5 citations
Computer Science · #Topic Modeling #Natural Language Processing Techniques #Speech Recognition and Synthesis
- Trimmed Density Ratio Estimation
2017/03/09 by Liu, Song, Takeda, Akiko, Suzuki, Taiji +1 · 2 citations
#FOS: Computer and information sciences #Machine Learning (stat.ML)
- State-Free Inference of State-Space Models: The Transfer Function Approach
2024/05/10 by Rom N. Parnichkun, Parnichkun, Rom N., Stefano Massaroli +23 · 5 citations
Engineering · #FOS: Computer and information sciences #FOS: Electrical engineering #Fault Detection and Control Systems #Machine Learning (cs.LG) #Systems and Control (eess.SY) #electronic engineering #information engineering
- Excess Risk of Two-Layer ReLU Neural Networks in Teacher-Student Settings and its Superiority to Kernel Methods
2022/05/30 by Shunta Akiyama, Akiyama, Shunta, Taiji Suzuki +1 · 3 citations
Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Stochastic Gradient Optimization Techniques
- Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillation
2025/02/02 by Juno Kim, Kim, Juno, Denny Wu +5 · 9 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computability, Logic, AI Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction
2023/06/12 by Taiji Suzuki, Denny Wu, Suzuki, Taiji +3 · 4 citations
Computer Science · Physics and Astronomy · Mathematics · #Stochastic Gradient Optimization Techniques #Model Reduction and Neural Networks #Markov Chains and Monte Carlo Methods
- Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization
2024/09/28 by Jiang, Jiarui, Huang, Wei, Zhang, Miao +2 · 5 citations
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Convex Tensor Decomposition via Structured Schatten Norm Regularization
2013/03/26 by Ryota Tomioka, Tomioka, Ryota, Taiji Suzuki +1 · 2 citations
Engineering · Mathematics · #Advanced Optimization Algorithms Research #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Numerical Analysis (math.NA) #Sparse and Compressive Sensing Techniques #Tensor decomposition and applications
- Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables
2017/11/06 by Masaaki Takada, Takada, Masaaki, Taiji Suzuki +3 · 2 citations
Computer Science · Engineering · Mathematics · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference
- Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
2022/03/19 by Sato, Kanji, Takeda, Akiko, Kawai, Reiichiro +1 · 2 citations
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Probability (math.PR)
- Understanding Generalization in Deep Learning via Tensor Methods
2020/01/14 by Jingling Li, Yanchao Sun, Li, Jingling +7 · 4 citations
Computer Science · Mathematics · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Computer and information sciences #I.2.2 #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Tensor decomposition and applications
- Condition Number Analysis of Kernel-based Density Ratio Estimation
2009/12/15 by Kanamori, Takafumi, Suzuki, Taiji, Sugiyama, Masashi · 1 citation
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Statistics Theory (math.ST)
- Regularization Strategies and Empirical Bayesian Learning for MKL
2010/11/13 by Tomioka, Ryota, Suzuki, Taiji · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Density-Difference Estimation
2012/06/30 by Masashi Sugiyama, Takafumi Kanamori, Sugiyama, Masashi +9 · 1 citation
Computer Science · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Target Tracking and Data Fusion in Sensor Networks
- Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
2024/06/17 by Kazusato Oko, Oko, Kazusato, Yujin Song +5 · 3 citations
Computer Science · #Neural Networks and Applications
- Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation
2013/04/25 by Song Liu, Liu, Song, John A. Quinn +7 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Machine Learning (stat.ML) #Metabolomics and Mass Spectrometry Studies #Statistical Methods and Inference
- Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
2018/08/26 by Taiji Suzuki, Hiroshi Abe, Suzuki, Taiji +15 · 2 citations
Engineering · Computer Science · #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference #Neural Networks and Applications
- Support Consistency of Direct Sparse-Change Learning in Markov Networks
2014/07/02 by Liu, Song, Suzuki, Taiji, Relator, Raissa +3 · 1 citation
#FOS: Computer and information sciences #Machine Learning (stat.ML)
- Learning Green's Function Efficiently Using Low-Rank Approximations
2023/08/01 by Kishan Wimalawarne, Wimalawarne, Kishan, Taiji Suzuki +3 · 2 citations
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Matrix Theory and Algorithms #Model Reduction and Neural Networks #Numerical Analysis (math.NA)
- Learning Sparse Structural Changes in High-dimensional Markov Networks: A Review on Methodologies and Theories
2017/01/06 by Song Liu, Kenji Fukumizu, Liu, Song +3 · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (stat.ML) #Machine Learning and Data Classification
- Cross-domain Recommendation via Deep Domain Adaptation
2018/03/08 by Heishiro Kanagawa, Hayato Kobayashi, Kanagawa, Heishiro +7 · 1 citation
Computer Science · #Computation and Language (cs.CL) #Expert finding and Q&A systems #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques #Topic Modeling
- Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
2018/09/05 by Murata, Tomoya, Suzuki, Taiji · 1 citation
#FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
- Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems
2023/12/02 by Juno Kim, Kim, Juno, Kakei Yamamoto +7 · 2 citations
Computer Science · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Stochastic Gradient Optimization Techniques
- Gradient Noise Convolution (GNC): Smoothing Loss Function for Distributed Large-Batch SGD
2019/06/26 by Haruki, Kosuke, Suzuki, Taiji, Hamakawa, Yohei +4 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Mean-field Analysis on Two-layer Neural Networks from a Kernel Perspective
2024/03/22 by Shokichi Takakura, Takakura, Shokichi, Taiji Suzuki +1 · 2 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics
2020/07/11 by Taiji Suzuki, Suzuki, Taiji · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Neural Networks and Applications #Stochastic Gradient Optimization Techniques
- Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding
2020/07/30 by Akira Nakagawa, Nakagawa, Akira, Keizo Kato +3 · 1 citation
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #I.2.4 #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks
- Estimation error analysis of deep learning on the regression problem on the variable exponent Besov space
2020/09/23 by Kazuma Tsuji, Taiji Suzuki, Tsuji, Kazuma +1 · 1 citation
Mathematics · Medicine · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mathematical Analysis and Transform Methods #Mathematical Approximation and Integration #Medical Imaging Techniques and Applications
- Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis
2020/12/31 by Atsushi Nitanda, Nitanda, Atsushi, Denny Wu +3 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
- High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
2024/06/05 by Yihang Chen, Fanghui Liu, Chen, Yihang +5 · 2 citations
Mathematics · Engineering · Computer Science · #Numerical methods in inverse problems #Sparse and Compressive Sensing Techniques #Gaussian Processes and Bayesian Inference
- A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?
2021/08/25 by Hiroaki Mikami, Kenji Fukumizu, Mikami, Hiroaki +13 · 1 citation
Computer Science · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG)
- Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization
2022/03/30 by Yuri Kinoshita, Taiji Suzuki, Kinoshita, Yuri +1 · 1 citation
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Probability (math.PR) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
- DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning
2023/02/08 by Tomoya Murata, Taiji Suzuki, Murata, Tomoya +1 · 1 citation
Computer Science · Medicine · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Pharmacological Effects and Toxicity Studies #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques
- Provably Transformers Harness Multi-Concept Word Semantics for Efficient In-Context Learning
2024/11/04 by Bu, Dake, Huang, Wei, Han, Andi +4 · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent
2024/10/07 by Bingrui Li, Wei Huang, Li, Bingrui +9 · 2 citations
Engineering · #Advanced Numerical Analysis Techniques #Advanced Research in Systems and Signal Processing #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optical Polarization and Ellipsometry
- State Space Models are Comparable to Transformers in Estimating Functions with Dynamic Smoothness
2024/05/29 by Naoki Nishikawa, Taiji Suzuki, Nishikawa, Naoki +1 · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
2025/10/13 by Wakayama, Tomoya, Suzuki, Taiji · 4 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
2025/01/09 by Juno Kim, Dimitri Meunier, Kim, Juno +7 · 2 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
- Direct Distributional Optimization for Provable Alignment of Diffusion Models
2025/02/05 by Ryotaro Kawata, Kawata, Ryotaro, Kazusato Oko +5 · 1 citation
Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Topology Optimization in Engineering
- Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble
2025/02/09 by Nitanda, Atsushi, Lee, Anzelle, Kai, Damian Tan Xing +2 · 1 citation
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- When Does Metadata Conditioning (NOT) Work for Language Model Pre-Training? A Study with Context-Free Grammars
2025/04/24 by Higuchi, Rei, Ryotaro Kawata, Naoki Nishikawa +16 · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Natural Language Processing Techniques #Topic Modeling
- Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric
2024/04/30 by Toshimitsu Uesaka, Taiji Suzuki, Uesaka, Toshimitsu +9 · 1 citation
Arts and Humanities · Psychology · #EFL/ESL Teaching and Learning #FOS: Computer and information sciences #Innovative Teaching and Learning Methods #Machine Learning (cs.LG)