Tommi S. Jaakkola

Tommi S. Jaakkola's AcademicInfluence.com Rankings

Tommi S. Jaakkola

Computer Science

#3549

World Rank

#3724

Historical Rank

Database

#888

World Rank

#933

Historical Rank

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Computer Science

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Tommi S. Jaakkola's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

An Introduction to Variational Methods for Graphical Models (1999) (4081)
Exploiting Generative Models in Discriminative Classifiers (1998) (1672)
Maximum-Margin Matrix Factorization (2004) (1151)
On the Convergence of Stochastic Iterative Dynamic Programming Algorithms (1993) (937)
Junction Tree Variational Autoencoder for Molecular Graph Generation (2018) (872)
A Deep Learning Approach to Antibiotic Discovery (2020) (832)
Weighted Low-Rank Approximations (2003) (803)
Serial Regulation of Transcriptional Regulators in the Yeast Cell Cycle (2001) (710)
MAP estimation via agreement on trees: message-passing and linear programming (2005) (709)
Partially labeled classification with Markov random walks (2001) (688)
Computational discovery of gene modules and regulatory networks (2003) (660)
Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms (2000) (641)
Rationalizing Neural Predictions (2016) (640)
Style Transfer from Non-Parallel Text by Cross-Alignment (2017) (639)
Analyzing Learned Molecular Representations for Property Prediction (2019) (615)
Bayesian parameter estimation via variational methods (2000) (610)
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation (2012) (598)
A Discriminative Framework for Detecting Remote Protein Homologies (2000) (568)
Towards Robust Interpretability with Self-Explaining Neural Networks (2018) (559)
Using the Fisher Kernel Method to Detect Remote Protein Homologies (1999) (490)
Fast optimal leaf ordering for hierarchical clustering (2001) (465)
A new class of upper bounds on the log partition function (2002) (462)
Mean Field Theory for Sigmoid Belief Networks (1996) (458)
Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks (2000) (451)
Learning Without State-Estimation in Partially Observable Markovian Decision Processes (1994) (430)
Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems (1994) (427)
Prediction of Organic Reaction Outcomes Using Machine Learning (2017) (427)
Discovery of non-directional and directional pioneer transcription factors by modeling DNase profile magnitude and shape (2014) (395)
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations (2007) (379)
Reinforcement Learning with Soft State Aggregation (1994) (367)
Tutorial on variational approximation methods (2000) (340)
Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models (2001) (326)
A graph-convolutional neural network model for the prediction of chemical reactivity† †Electronic supplementary information (ESI) available: Additional model and dataset details, results, discussion, and ref. 38 and 39. See DOI: 10.1039/c8sc04228d (2018) (323)
On the Robustness of Interpretability Methods (2018) (319)
Tightening LP Relaxations for MAP using Message Passing (2008) (317)
Maximum Entropy Discrimination (1999) (294)
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction. (2017) (289)
Continuous Representations of Time-Series Gene Expression Data (2003) (285)
Probabilistic kernel regression models (1999) (282)
Tree-based reparameterization framework for analysis of sum-product and related algorithms (2003) (278)
Gromov-Wasserstein Alignment of Word Embedding Spaces (2018) (242)
Tree-reweighted belief propagation algorithms and approximate ML estimation by pseudo-moment matching (2003) (236)
MAP estimation via agreement on (hyper)trees: Message-passing and linear programming (2005) (231)
A new approach to analyzing gene expression time series data (2002) (229)
A variational approach to Bayesian logistic regression problems and their extensions (1996) (224)
Learning Bayesian Network Structure using LP Relaxations (2010) (217)
Generative Models for Graph-Based Protein Design (2019) (213)
Dual Decomposition for Parsing with Non-Projective Head Automata (2010) (192)
Variational Probabilistic Inference and the QMR-DT Network (2011) (189)
On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing (2010) (187)
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network (2017) (186)
Physical Network Models (2004) (181)
Generalization and Representational Limits of Graph Neural Networks (2020) (179)
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture (2017) (176)
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018) (166)
A causal framework for explaining the predictions of black-box sequence-to-sequence models (2017) (165)
Introduction to dual composition for inference (2011) (159)
New Outer Bounds on the Marginal Polytope (2007) (158)
Tree consistency and bounds on the performance of the max-product algorithm and its generalizations (2004) (144)
Hierarchical Generation of Molecular Graphs using Structural Motifs (2020) (142)
Molding CNNs for text: non-linear, non-consecutive convolutions (2015) (141)
Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes (2003) (141)
Bayesian Network Approach to Cell Signaling Pathway Modeling (2002) (138)
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data (2002) (137)
Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices (2004) (128)
Maximum-likelihood estimation of optimal scaling factors for expression array normalization (2001) (124)
Information Regularization with Partially Labeled Data (2002) (124)
Variational methods for inference and estimation in graphical models (1997) (122)
Deriving Neural Architectures from Sequence and Graph Kernels (2017) (119)
Low-Rank Tensors for Scoring Dependency Structures (2014) (115)
Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings (2016) (107)
Collaborative future event recommendation (2010) (106)
Validation and refinement of gene-regulatory pathways on a network of physical interactions (2005) (105)
On the Dirichlet Prior and Bayesian Regularization (2002) (102)
Multi-Objective Molecule Generation using Interpretable Substructures (2020) (102)
High-resolution computational models of genome binding events (2006) (98)
Approximating Posterior Distributions in Belief Networks Using Mixtures (1997) (98)
Tree-structured decoding with doubly-recurrent neural networks (2016) (96)
Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis (2020) (96)
Invariant Rationalization (2020) (94)
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers (2019) (91)
Tractable Bayesian learning of tree belief networks (2000) (90)
Feature Selection and Dualities in Maximum Entropy Discrimination (2000) (90)
Aspect-augmented Adversarial Networks for Domain Adaptation (2017) (89)
On Information Regularization (2002) (89)
Learning Efficiently with Approximate Inference via Dual Losses (2010) (87)
Bayesian Methods for Elucidating Genetic Regulatory Networks (2002) (84)
On the Partition Function and Random Maximum A-Posteriori Perturbations (2012) (84)
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures (2017) (84)
Semi-supervised Question Retrieval with Gated Convolutions (2015) (84)
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (2019) (83)
Tree Block Coordinate Descent for MAP in Graphical Models (2009) (81)
Computing upper and lower bounds on likelihoods in intractable networks (1996) (79)
Perspectives on ENCODE (2020) (79)
Approximate inference in graphical models using lp relaxations (2010) (77)
Improving the Mean Field Approximation Via the Use of Mixture Distributions (1999) (75)
Word Embeddings as Metric Recovery in Semantic Spaces (2016) (75)
A Deep Learning Approach to Antibiotic Discovery (2020) (74)
Tree-based reparameterization for approximate inference on loopy graphs (2001) (72)
Steps Toward Deep Kernel Methods from Infinite Neural Networks (2015) (71)
Using term informativeness for named entity detection (2005) (70)
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction (2022) (69)
Grounding Language for Transfer in Deep Reinforcement Learning (2017) (69)
An Unsupervised Method for Uncovering Morphological Chains (2015) (67)
Online Learning of Non-stationary Sequences (2003) (64)
The Variational Homoencoder: Learning to learn high capacity generative models from few examples (2018) (59)
Approximate inference using planar graph decomposition (2006) (56)
Torsional Diffusion for Molecular Conformer Generation (2022) (56)
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking (2021) (56)
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations (2013) (55)
Greed is Good if Randomized: New Inference for Dependency Parsing (2014) (55)
Towards Optimal Transport with Global Invariances (2018) (55)
Discovering homotypic binding events at high spatial resolution (2010) (54)
Deep learning identifies synergistic drug combinations for treating COVID-19 (2021) (52)
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021) (49)
Blank Language Models (2020) (49)
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022) (48)
Parameter Expanded Variational Bayesian Methods (2006) (48)
Bias-Corrected Bootstrap and Model Uncertainty (2003) (48)
Distributed Information Regularization on Graphs (2004) (48)
Crystal Diffusion Variational Autoencoder for Periodic Material Generation (2021) (48)
Structured Optimal Transport (2017) (46)
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles (2021) (45)
Controlling privacy in recommender systems (2014) (45)
Continuation Methods for Mixing Heterogenous Sources (2002) (45)
Recursive Algorithms for Approximating Probabilities in Graphical Models (1996) (44)
Approximate inference using conditional entropy decompositions (2007) (43)
DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022) (43)
Learning Population-Level Diffusions with Generative RNNs (2016) (42)
Kernel Expansions with Unlabeled Examples (2000) (42)
Automated Discovery of Functional Generality of Human Gene Expression Programs (2007) (41)
Path-Augmented Graph Transformer Network (2019) (41)
Convergent Propagation Algorithms via Oriented Trees (2007) (38)
Educating Text Autoencoders: Latent Representation Guidance via Denoising (2019) (38)
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees (2014) (37)
Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models (2022) (36)
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions (2015) (33)
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms (2012) (33)
Exact MAP Estimates by (Hyper)tree Agreement (2002) (33)
Towards Robust, Locally Linear Deep Networks (2019) (33)
Direct Optimization through arg max for Discrete Variational Auto-Encoder (2018) (32)
Variational methods and the QMR-DT database (1998) (32)
Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks (1995) (32)
Sequentially Fitting "Inclusive" Trees for Inference in Noisy-OR Networks (2000) (32)
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery (2022) (31)
Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees (2014) (31)
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis (2019) (30)
A Game Theoretic Approach to Class-wise Selective Rationalization (2019) (30)
Variational probabilistic inference and the QMR-DT database (1998) (30)
Efficient Conformal Prediction via Cascaded Inference with Expanded Admission (2021) (27)
Generative models for molecular discovery: Recent advances and challenges (2022) (27)
Active Information Retrieval (2001) (26)
Are Learned Molecular Representations Ready For Prime Time? (2019) (26)
Stable Mixing of Complete and Incomplete Information (2001) (26)
Few-shot Conformal Prediction with Auxiliary Tasks (2021) (25)
Predictive Discretization during Model Selection (2004) (25)
Learning with Maximum A-Posteriori Perturbation Models (2014) (25)
Learning Task Informed Abstractions (2021) (25)
Time series analysis of gene expression and location data (2003) (24)
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models (2013) (24)
Clusters and Coarse Partitions in LP Relaxations (2008) (24)
Physical network models and multi-source data integration (2003) (23)
More data means less inference: A pseudo-max approach to structured learning (2010) (23)
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions (2013) (22)
Integration of Principal-Component-Analysis and Streamline Information for the History Matching of Channelized Reservoirs (2014) (22)
Unsupervised Active Learning in Large Domains (2002) (22)
Deep Transfer in Reinforcement Learning by Language Grounding (2017) (22)
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces (2019) (22)
Hierarchical Graph-to-Graph Translation for Molecules (2019) (22)
Consistent Accelerated Inference via Confident Adaptive Transformers (2021) (21)
Domain Extrapolation via Regret Minimization (2020) (21)
Metric recovery from directed unweighted graphs (2014) (20)
Composing Molecules with Multiple Property Constraints (2020) (20)
Is Conditional Generative Modeling all you need for Decision-Making? (2022) (19)
Optimal Transport Graph Neural Networks (2020) (18)
Mean Field Theory for Sigmoid Belief NetworksMean Field Theory for Sigmoid Belief (1996) (17)
From random walks to distances on unweighted graphs (2015) (17)
Modeling the Combinatorial Functions of Multiple Transcription Factors (2005) (16)
Multi-resolution Autoregressive Graph-to-Graph Translation for Molecules (2019) (16)
Information Obfuscation of Graph Neural Networks (2020) (16)
Learning Tree Structured Potential Games (2016) (15)
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations (2022) (15)
Improving Molecular Design by Stochastic Iterative Target Augmentation (2020) (15)
Functional Transparency for Structured Data: a Game-Theoretic Approach (2019) (15)
Bayesian parameter estimation through variational methods (2008) (15)
A synergistic DNA logic predicts genome-wide chromatin accessibility (2016) (14)
Poisson Flow Generative Models (2022) (14)
Integration of PCA with a Novel Machine Learning Method for Reparameterization and Assisted History Matching Geologically Complex Reservoirs (2015) (14)
On Measure Concentration of Random Maximum A-Posteriori Perturbations (2013) (14)
Active Boundary Annotation using Random MAP Perturbations (2014) (13)
Denoising Bodies to Titles: Retrieving Similar Questions with Recurrent Convolutional Models (2015) (13)
Managing the 802.11 Energy/Performance Tradeoff with Machine Learning (2004) (13)
Learning Representations that Support Robust Transfer of Predictors (2021) (12)
Fundamental Limits and Tradeoffs in Invariant Representation Learning (2020) (12)
Understanding Interlocking Dynamics of Cooperative Rationalization (2021) (12)
Learning population-level diffusions with generative recurrent networks (2016) (12)
Generalized Low-Rank Approximations (2003) (11)
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling (2019) (11)
Solving graph compression via optimal transport (2019) (11)
Clustering and efficient use of unlabeled examples (2001) (10)
Learning to refine text based recommendations (2016) (10)
Correction to Analyzing Learned Molecular Representations for Property Prediction (2019) (10)
Structured Prediction: From Gaussian Perturbations to Linear-Time Principled Algorithms (2015) (10)
Enforcing Predictive Invariance across Structured Biomedical Domains (2020) (9)
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training (2021) (9)
Data-Dependent Regularization (2006) (9)
Linear Dependent Dimensionality Reduction (2003) (9)
Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo (2006) (9)
Word, graph and manifold embedding from Markov processes (2015) (9)
Oblique Decision Trees from Derivatives of ReLU Networks (2019) (9)
Large-Margin Matrix Factorization (2004) (8)
Primal-Dual methods for sparse constrained matrix completion (2012) (8)
Learning Optimal Interventions (2016) (8)
Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning (2022) (7)
A Stratified Approach to Robustness for Randomly Smoothed Classifiers (2019) (7)
Automatic Feature Induction for Text Classification (2002) (7)
Local Aggregative Games (2017) (7)
Antibody-Antigen Docking and Design via Hierarchical Structure Refinement (2022) (7)
Learning Corresponded Rationales for Text Matching (2018) (7)
High Dimensional Inference With Random Maximum A-Posteriori Perturbations (2016) (7)
Thinking Outside the Box: Enhancing Science Teaching by Combining (Instead of Contrasting) Laboratory and Simulation Activities (2012) (6)
Adaptive Invariance for Molecule Property Prediction (2020) (6)
Controlling Directions Orthogonal to a Classifier (2022) (6)
Sparse Matrix Factorization of Gene Expression Data (2001) (6)
Tree-based reparameterization analysis of belief propagation and related algorithms for approximate inference on graphs with cycles (2002) (6)
Graph Adversarial Networks: Protecting Information against Adversarial Attacks (2020) (6)
Hierarchical Dirichlet Process-Based Models For Discovery of Cross-species Mammalian Gene Expression (2007) (6)
Self-Supervised Learning of Appliance Usage (2020) (6)
Statistical Learning under Nonstationary Mixing Processes (2015) (6)
Modeling Persistent Trends in Distributions (2015) (5)
Game-Theoretic Interpretability for Temporal Modeling (2018) (5)
Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis (2020) (5)
Conformal Prediction Sets with Limited False Positives (2022) (5)
A Unified Framework for Consistency of Regularized Loss Minimizers (2014) (5)
Fragment-based Sequential Translation for Molecular Optimization (2021) (4)
Erratum: High-resolution computational models of genome binding events (2006) (4)
Sparse Matrix Factorization for Analyzing Gene Expression Patterns (2001) (4)
Lineage-based identification of cellular states and expression programs (2012) (4)
Two-Sided Exponential Concentration Bounds for Bayes Error Rate and Shannon Entropy (2013) (4)
Adversarial Support Alignment (2022) (4)
Game Theoretic Algorithms for Protein-DNA binding (2006) (4)
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models (2023) (3)
Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (2005) (2012) (3)
Syfer: Neural Obfuscation for Private Data Release (2022) (3)
Modeling Drug Combinations based on Molecular Structures and Biological Targets (2020) (3)
Inferring regulatory networks from multiple sources of genomic data (2004) (3)
CRAFT: ClusteR-specific Assorted Feature selecTion (2015) (3)
Focused Inference (2005) (3)
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement (2022) (3)
Latent Space Secrets of Denoising Text-Autoencoders (2019) (3)
Generating Molecules with Optimized Aqueous Solubility using Iterative Graph Translation (2021) (3)
Approximate Expectation Propagation for Bayesian Inference on Large-scale Problems (2005) (2)
Modeling Trends in Distributions (2015) (2)
Calibrated Selective Classification (2022) (2)
Stable Target Field for Reduced Variance Score Estimation in Diffusion Models (2023) (2)
6.867 Machine Learning, Fall 2002 (2002) (2)
Locally Constant Networks (2019) (2)
DNA Binding and Games (2006) (2)
SE(3) diffusion model with application to protein backbone generation (2023) (2)
Proceedings of the Fifth European Workshop on Probabilistic Graphical Models (2010) (2)
Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models (2020) (2)
Table 2 (Supplemental): Complete data for all 100 expression programs discovered by GeneProgram from the Novartis Gene Atlas v2 (2007) (1)
Table 1 (Supplemental): Summary of expression programs discovered by GeneProgram from Novartis Tissue Atlas v2 data (2007) (1)
Dna binding economies (2007) (1)
Predicting deliberative outcomes (2020) (1)
On Iteratively Constraining the Marginal Polytope for Approximate Inference and MAP (2007) (1)
Efficiently Controlling Multiple Risks with Pareto Testing (2022) (1)
END-TO-END RIGID PROTEIN DOCKING (2021) (1)
The Kaali giant meteorite fall in the Finnish-Estonian folklore. (1988) (1)
On the Statistical Efficiency of $\ell_{1,p}$ Multi-Task Learning of Gaussian Graphical Models (2012) (1)
To appear in "Semi-supervised learning", O. Chappelle, B. Scholkopf, and A. Zien, Eds., 2005 (2005) (1)
Using Deep Reinforcement Learning to Generate Rationales for Molecules (2018) (1)
LATION FOR MOLECULAR OPTIMIZATION (2019) (1)
Fragment-based Sequential Translation for Molecular Optimization FRAGMENT-BASED SEQUENTIAL TRANSLATION FOR MOLECULAR OPTIMIZATION (2021) (1)
T REE-STRUCTURED DECODING WITH DOUBLY-RECURRENT NEURAL NETWORKS (2017) (1)
Analysis of signaling pathways in human T-cells using Bayesian network modeling of single cell data (2004) (1)
Relaxed Conformal Prediction Cascades for Efficient Inference Over Many Labels (2020) (1)
Iterative Target Augmentation for Effective Conditional Generation (2019) (0)
EigenFold: Generative Protein Structure Prediction with Diffusion Models (2023) (0)
Virtual Node Graph Neural Network for Full Phonon Prediction (2023) (0)
Adaptive Invariant Risk Minimization for Molecule Property Prediction (2020) (0)
Vc Dimension Further Information 3.3 Optimization Algorithms 3 Learning from Data (1996) (0)
The Benefits of Pairwise Discriminators for Adversarial Training (2020) (0)
Uncertainty in artificial intelligence : proceedings of the Twenty-first Conference (2005) : July 26-29, 2005, Edinburgh, Scotland (2005) (0)
A Comprehensive Approach to Fusion for Microsensor Networks: Distributed and Hierarchical Inference, Communication, and Adaption REPORT DOCUMENTATION PAGE (2000) (0)
GenPhys: From Physical Processes to Generative Models (2023) (0)
Denoising Improves Latent Space Geometry in Text Autoencoders (2019) (0)
IMPROVING THE MEAN FIELD APPROXIMATIONVIA THE USE OF MIXTURE (1998) (0)
Learning with Online Constraints : Shifting Concepts and Active Learning (0)
MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES (1996) (0)
Author Correction: Perspectives on ENCODE (2022) (0)
Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models (2023) (0)
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models (2023) (0)
Automated System for Knowledge-Based Continuous Organic Synthesis: Data-Driven Pathway Design and Validation (Invited Lecture) (2018) (0)
A ug 2 00 5 1 MAP estimation via agreement on trees : Message-passing and linear programming (0)
An Unsupervised Method for Uncovering Morphological Chains (Open Access, Publisher's Version) (2015) (0)
Special Issue on the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010) (2012) (0)
Tradeo s between generative and discriminative hidden Markov models (2006) (0)
Adaptive Information Filtering with Minimal Instruction MIT 2000-08 Progress Report : January 1 , 2002 — June 30 , 2002 Tommi Jaakkola and Tomaso Poggio Project Overview (2003) (0)
Predicting Synergistic Drug Combinations for COVID with Biological Bottleneck Models (2020) (0)
Author Correction: Expanded encyclopaedias of DNA elements in the human and mouse genomes (2022) (0)
Tree-based reparameterization forapproximate estimation on loopy graphsMartin (2001) (0)
Generalized Low-Rank Approximations Nathan Srebro and Tommi Jaakkola (2003) (0)
T ORSIONAL D IFFUSION FOR M OLECULAR C ONFORMER G ENERATION (2022) (0)
6 . 867 Machine Learning Lecture 20 (2010) (0)
Controlling privacy in recommender systems Citation (2015) (0)
Bidirectional Inference Networks with Application to Health Profiling (2019) (0)
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels (2023) (0)
ANALYSIS OF $sup 55$Fe PRODUCED BY NUCLEAR TESTS AND ITS ENRICHMENT IN FINNISH LAPPS. (1969) (0)
A re-examination of Danjon's observations of the planet Mercury. (1969) (0)
STAT 538 Handout 5 February 21 , 2008 Variational bounds for graphical models (0)
Food Adulteration Detection Using Neural Networks by Youyang Gu (2016) (0)
Blind protein-ligand docking with diffusion-based deep generative models. (2023) (0)
3 Extractive Rationale Generation (2016) (0)
Small scale validation cont (2010) (0)
Structured Optimal Transport : Supplementary Material (2018) (0)
Improving Multi-class Text Classification with Naive Bayes Improving Multi-class Text Classification with Naive Bayes Contents (2001) (0)
Maximum Likelihood Markov Hypertrees (2001) (0)
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition (2018) (0)
Artificial Intelligence and Statistics 2001 : proceedings of the eighth international workshop : January 4-7, 2001, Key West, Florida (2001) (0)
Supplementary materials and proofs (2015) (0)
A Dealing with di ↵ erent samples sizes and dimensions (2019) (0)
Bringing Simulations to the Classroom: Teachers’ Perspectives (2021) (0)
Training and test performance : sampling (2004) (0)
Strategic Prediction with Latent Aggregative Games (2019) (0)
6.867 Machine Learning Lecture 16 (2010) (0)

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Tommi S. Jaakkola is affiliated with the following schools:

Massachusetts Institute of Technology