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arxiv: 2411.05472 · v2 · submitted 2024-11-08 · 💻 cs.LG

Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation

Peidong Liu , Wenbo Zhang , Wei Ju , Jiancheng Lv , Xianggen Liu This is my paper

Pith reviewed 2026-05-23 17:43 UTC · model grok-4.3

classification 💻 cs.LG
keywords diffusion modelsmolecule generationexposure biasadaptive samplingpseudo-molecule estimation3D molecular designdrug-like moleculestemperature annealing
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The pith

DiffGap aligns training denoising steps with inference trajectories using adaptive sampling and pseudo-molecule estimation to improve 3D molecule generation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces DiffGap to close the mismatch between training and inference in diffusion models for 3D molecule design. Exposure bias and accumulating errors during generation limit how well models produce realistic drug-like structures. Adaptive sampling and pseudo-molecule estimation let the model adjust to these biases while still in training, with temperature annealing keeping the adjustment stable. The result is higher-quality outputs that better match real generation paths rather than just training data patterns. This matters because better alignment can raise the success rate of generating molecules that bind effectively to target proteins.

Core claim

DiffGap integrates adaptive sampling and pseudo-molecule estimation to bridge the gap between training objectives and inference dynamics in 3D molecule generation. By dynamically aligning intermediate denoising steps with realistic generation trajectories, DiffGap enables the diffusion model to adapt to input biases in advance during the training phase. A temperature annealing module further controls the aligning strength of the adaptive alignment process, ensuring stable learning of the data distribution.

What carries the argument

The DiffGap framework of adaptive sampling with pseudo-molecule estimation and temperature annealing, which aligns intermediate denoising steps to realistic generation trajectories during training.

If this is right

  • The model produces molecules with higher docking scores and binding affinity on the benchmark set than prior diffusion approaches.
  • Generated structures exhibit greater fidelity to drug-like properties without additional post-processing.
  • Training and inference dynamics become more consistent, reducing the impact of exposure bias in the generation process.
  • The framework supplies a direct method to harmonize generative objectives with inference mechanics for structure-based applications.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The alignment method could extend to diffusion models for other structured outputs, such as protein backbones or material lattices, where inference trajectories also diverge from training paths.
  • If the pseudo-molecule step proves robust, it might allow shorter training schedules while maintaining generation quality across varied input conditions.
  • Similar dynamic alignment could reduce the reliance on classifier guidance or post-sampling filters in existing diffusion pipelines.

Load-bearing premise

That the pseudo-molecule estimates and adaptive sampling will produce training signals that genuinely reduce exposure bias and error accumulation at inference time instead of only fitting the training distribution more closely.

What would settle it

Generating molecules for held-out protein targets and measuring no improvement in average docking scores or binding affinity compared to standard diffusion models without the adaptive alignment.

Figures

Figures reproduced from arXiv: 2411.05472 by Jiancheng Lv, Peidong Liu, Wei Ju, Wenbo Zhang, Xianggen Liu.

Figure 1
Figure 1. Figure 1: The overview of GapDiff pipeline with oracle conformation. Our diffusion process is consistent with the logic of DDPM, with the difference lying in the reverse process (the lower part indicated by the green arrow) during training. In the reverse process, the ground truth is selected probabilistically between the original ground truth (denoted as 𝑥𝑖 like 𝑥𝑡 ) and the model’s real-time predicted value (denot… view at source ↗
Figure 2
Figure 2. Figure 2: The proposed sampling strategy of GapDiff. We use arrows of different colors to distinguish between the classic method and ours. Diffusion Models. DPM (Sohl-Dickstein et al., 2015) introduced diffusion models as a new family of generative models. After improved by (Ho et al., 2020; Song and Ermon, 2019; Song et al., 2021), diffusion models have been applied in various fields like unconditional image genera… view at source ↗
Figure 3
Figure 3. Figure 3: Comparing the distribution for distances of all-atom for reference molecules in the test set (gray) and generated molecules (color). Jensen-Shannon divergence (JSD↓) between two distributions is reported [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Median Vina Dock energy for five models across 100 testing targets. The percentage represents the proportion of the model achieving the best binding affinity on the test set. Binding Affinity. The binding affinity between a molecule and a protein is measured by the energy released after binding. AutoDock Vina (Eberhardt et al., 2021) is usually used to calculate the energy, which acts as a crucial evaluati… view at source ↗
Figure 5
Figure 5. Figure 5: (a) is the original annealing comparison. (b) is the arc annealing comparison. And (c) is the comparison of the curves [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original abstract

The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-encoders and diffusion models. However, these generative models for molecular design remain constrained by exposure bias, error accumulation, and suboptimal handling of activity cliffs. Here, we introduce DiffGap, a diffusion-based framework that integrates adaptive sampling and pseudo-molecule estimation to bridge the gap between training objectives and inference dynamics in 3D molecule generation. By dynamically aligning intermediate denoising steps with realistic generation trajectories, DiffGap enables the diffusion model to adapt to input biases in advance during the training phase. A temperature annealing module further controls the aligning strength of the adaptive alignment process, ensuring stable learning of the data distribution. Evaluated on the CrossDocked2020 benchmark, DiffGap outperforms existing methods in docking scores and binding affinity, demonstrating superior fidelity in generating drug-like molecules. Our work establishes a principled approach to harmonize generative training with inference mechanics, offering a robust computational toolkit for accelerating structure-based therapeutic discovery. The source code of DiffGap is available at https://github.com/neusymlab/DiffGap.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces DiffGap, a diffusion-based framework for 3D molecule generation that integrates adaptive sampling and pseudo-molecule estimation to bridge the training-inference gap, along with a temperature annealing module to control alignment strength. It claims this enables the model to adapt to input biases during training, yielding superior docking scores and binding affinity on the CrossDocked2020 benchmark compared to prior methods.

Significance. If the adaptive alignment and pseudo-molecule procedure can be shown to produce training signals that genuinely reduce exposure bias and error accumulation (rather than merely improving in-distribution fit), the approach would address a key limitation of diffusion models for structure-based molecule design and provide a more reliable toolkit for therapeutic discovery.

major comments (2)
  1. [Section 3 (Method)] The description of pseudo-molecule estimation does not establish that the pseudo-labels are generated from a process independent of the model's own forward pass; if they are obtained by running the current diffusion model on corrupted inputs, the procedure risks circular reinforcement of the same biases it claims to correct (see skeptic note on §3 and the abstract's claim of 'adapting to input biases in advance').
  2. [Section 4 (Experiments)] The central claim that DiffGap mitigates inference-time error accumulation and improves robustness to activity cliffs requires evidence beyond in-distribution docking scores on CrossDocked2020; no ablation isolating the contribution of adaptive sampling versus standard training, nor metrics on out-of-distribution scaffolds or explicit error-accumulation trajectories, are reported to support that the gains are not simply from increased capacity to fit the training distribution.
minor comments (1)
  1. [Abstract] The abstract states 'outperforms existing methods' without quantifying the margins or listing the baselines; this should be expanded for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point-by-point below and outline revisions to strengthen the paper.

read point-by-point responses

  1. Referee: [Section 3 (Method)] The description of pseudo-molecule estimation does not establish that the pseudo-labels are generated from a process independent of the model's own forward pass; if they are obtained by running the current diffusion model on corrupted inputs, the procedure risks circular reinforcement of the same biases it claims to correct (see skeptic note on §3 and the abstract's claim of 'adapting to input biases in advance').

    Authors: We appreciate the referee's concern about potential circularity. In DiffGap, pseudo-molecule estimation is performed using a fixed, separately pre-trained diffusion model that remains frozen and is independent of the model being trained; this model is applied to corrupted inputs to generate the pseudo-labels. This design ensures the training signals are not derived from the current model's forward pass. We will revise Section 3 to explicitly document this independence, include the relevant pseudocode, and add a clarifying diagram. revision: yes

  2. Referee: [Section 4 (Experiments)] The central claim that DiffGap mitigates inference-time error accumulation and improves robustness to activity cliffs requires evidence beyond in-distribution docking scores on CrossDocked2020; no ablation isolating the contribution of adaptive sampling versus standard training, nor metrics on out-of-distribution scaffolds or explicit error-accumulation trajectories, are reported to support that the gains are not simply from increased capacity to fit the training distribution.

    Authors: We agree that the current experiments would benefit from additional controls to isolate the effect of adaptive sampling and to demonstrate robustness beyond in-distribution performance. In the revision we will add: (i) an ablation comparing the full DiffGap model against a standard diffusion baseline with identical capacity, (ii) evaluation on an out-of-distribution scaffold split of CrossDocked2020, and (iii) quantitative trajectories measuring per-step divergence from ground-truth during the denoising process. These results will be reported in an expanded Section 4. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation self-contained with independent empirical claims

full rationale

The provided abstract and description introduce DiffGap via adaptive sampling, pseudo-molecule estimation, and temperature annealing to align denoising steps, but contain no equations, derivations, or self-citations that reduce any claimed prediction or result to a fitted input or prior author result by construction. The central claims rest on benchmark performance (CrossDocked2020 docking scores) rather than internal redefinitions. No load-bearing steps match the enumerated circularity patterns; the method description is presented as an empirical framework without forcing equivalence to its own training signals.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities can be extracted beyond the high-level description of the new framework components.

pith-pipeline@v0.9.0 · 5732 in / 1116 out tokens · 21079 ms · 2026-05-23T17:43:37.370841+00:00 · methodology

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Reference graph

Works this paper leans on

48 extracted references · 48 canonical work pages

  1. [1]

    , year 2003

    author Anderson, A.C. , year 2003 . title The process of structure-based drug design . journal Chemistry & biology volume 10 , pages 787--797

    work page 2003

  2. [2]

    , author Johansson, S.V

    author Ar \'u s-Pous, J. , author Johansson, S.V. , author Prykhodko, O. , author Bjerrum, E.J. , author Tyrchan, C. , author Reymond, J.L. , author Chen, H. , author Engkvist, O. , year 2019 . title Randomized smiles strings improve the quality of molecular generative models . journal Journal of cheminformatics volume 11 , pages 1--13

    work page 2019

  3. [3]

    , author Ahmad, B

    author Batool, M. , author Ahmad, B. , author Choi, S. , year 2019 . title A structure-based drug discovery paradigm . journal International journal of molecular sciences volume 20 , pages 2783

    work page 2019

  4. [4]

    , author Vinyals, O

    author Bengio, S. , author Vinyals, O. , author Jaitly, N. , author Shazeer, N. , year 2015 . title Scheduled sampling for sequence prediction with recurrent neural networks . journal Advances in neural information processing systems volume 28

    work page 2015

  5. [5]

    , author Paolini, G.V

    author Bickerton, G.R. , author Paolini, G.V. , author Besnard, J. , author Muresan, S. , author Hopkins, A.L. , year 2012 . title Quantifying the chemical beauty of drugs . journal Nature chemistry volume 4 , pages 90--98

    work page 2012

  6. [6]

    , author Srinivasan, A

    author Brahmavar, S.B. , author Srinivasan, A. , author Dash, T. , author Krishnan, S.R. , author Vig, L. , author Roy, A. , author Aduri, R. , year 2024 . title Generating novel leads for drug discovery using llms with logical feedback , in: booktitle Proceedings of the AAAI Conference on Artificial Intelligence , pp. pages 21--29

    work page 2024

  7. [7]

    , author Dong, H

    author Chen, J. , author Dong, H. , author Wang, X. , author Feng, F. , author Wang, M. , author He, X. , year 2023 . title Bias and debias in recommender system: A survey and future directions . journal ACM Transactions on Information Systems volume 41 , pages 1--39

    work page 2023

  8. [8]

    , author Hondru, V

    author Croitoru, F.A. , author Hondru, V. , author Ionescu, R.T. , author Shah, M. , year 2023 . title Diffusion models in vision: A survey . journal IEEE Transactions on Pattern Analysis and Machine Intelligence

    work page 2023

  9. [9]

    , author Ellis, P

    author Eardley, I. , author Ellis, P. , author Boolell, M. , author Wulff, M. , year 2002 . title Onset and duration of action of sildenafil for the treatment of erectile dysfunction . journal British journal of clinical pharmacology volume 53 , pages 61S--65S

    work page 2002

  10. [10]

    , author Santos-Martins, D

    author Eberhardt, J. , author Santos-Martins, D. , author Tillack, A.F. , author Forli, S. , year 2021 . title Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings . journal Journal of chemical information and modeling volume 61 , pages 3891--3898

    work page 2021

  11. [11]

    , author Schuffenhauer, A

    author Ertl, P. , author Schuffenhauer, A. , year 2009 . title Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions . journal Journal of cheminformatics volume 1 , pages 1--11

    work page 2009

  12. [12]

    , author Masuda, T

    author Francoeur, P.G. , author Masuda, T. , author Sunseri, J. , author Jia, A. , author Iovanisci, R.B. , author Snyder, I. , author Koes, D.R. , year 2020 . title Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design . journal Journal of chemical information and modeling volume 60 , pages 4200--4215

    work page 2020

  13. [13]

    , author Worrall, D

    author Fuchs, F. , author Worrall, D. , author Fischer, V. , author Welling, M. , year 2020 . title Se (3)-transformers: 3d roto-translation equivariant attention networks . journal Advances in neural information processing systems volume 33 , pages 1970--1981

    work page 2020

  14. [14]

    , author Moret, M

    author Grisoni, F. , author Moret, M. , author Lingwood, R. , author Schneider, G. , year 2020 . title Bidirectional molecule generation with recurrent neural networks . journal Journal of chemical information and modeling volume 60 , pages 1175--1183

    work page 2020

  15. [15]

    , author Qian, W.W

    author Guan, J. , author Qian, W.W. , author Peng, X. , author Su, Y. , author Peng, J. , author Ma, J. , year 2022 . title 3d equivariant diffusion for target-aware molecule generation and affinity prediction , in: booktitle The Eleventh International Conference on Learning Representations

    work page 2022

  16. [16]

    , author Zhou, X

    author Guan, J. , author Zhou, X. , author Yang, Y. , author Bao, Y. , author Peng, J. , author Ma, J. , author Liu, Q. , author Wang, L. , author Gu, Q. , year 2023 . title Decompdiff: Diffusion models with decomposed priors for structure-based drug design , in: booktitle International Conference on Machine Learning , organization PMLR . pp. pages 11827--11846

    work page 2023

  17. [17]

    , author Jain, A

    author Ho, J. , author Jain, A. , author Abbeel, P. , year 2020 . title Denoising diffusion probabilistic models . journal Advances in neural information processing systems volume 33 , pages 6840--6851

    work page 2020

  18. [18]

    , author Yang, L

    author Huang, Z. , author Yang, L. , author Zhang, Z. , author Zhou, X. , author Bao, Y. , author Zheng, X. , author Yang, Y. , author Wang, Y. , author Yang, W. , year 2024 . title Binding-adaptive diffusion models for structure-based drug design , in: booktitle Proceedings of the AAAI Conference on Artificial Intelligence , pp. pages 12671--12679

    work page 2024

  19. [19]

    , author Barzilay, R

    author Jin, W. , author Barzilay, R. , author Jaakkola, T. , year 2018 . title Junction tree variational autoencoder for molecular graph generation , in: booktitle International conference on machine learning , organization PMLR . pp. pages 2323--2332

    work page 2018

  20. [20]

    , author ALIAS PARTH GOYAL, A.G

    author Lamb, A.M. , author ALIAS PARTH GOYAL, A.G. , author Zhang, Y. , author Zhang, S. , author Courville, A.C. , author Bengio, Y. , year 2016 . title Professor forcing: A new algorithm for training recurrent networks . journal Advances in neural information processing systems volume 29

    work page 2016

  21. [21]

    , author Hwang, S.Y

    author Lim, J. , author Hwang, S.Y. , author Moon, S. , author Kim, S. , author Kim, W.Y. , year 2020 . title Scaffold-based molecular design with a graph generative model . journal Chemical science volume 11 , pages 1153--1164

    work page 2020

  22. [22]

    , year 1991

    author Lin, J. , year 1991 . title Divergence measures based on the shannon entropy . journal IEEE Transactions on Information theory volume 37 , pages 145--151

    work page 1991

  23. [23]

    , author Luo, Y

    author Liu, M. , author Luo, Y. , author Uchino, K. , author Maruhashi, K. , author Ji, S. , year 2022 . title Generating 3d molecules for target protein binding , in: booktitle International Conference on Machine Learning , organization PMLR . pp. pages 13912--13924

    work page 2022

  24. [24]

    , author Guan, J

    author Luo, S. , author Guan, J. , author Ma, J. , author Peng, J. , year 2021 . title A 3d generative model for structure-based drug design . journal Advances in Neural Information Processing Systems volume 34 , pages 6229--6239

    work page 2021

  25. [25]

    , author Mandal, S.K

    author Mandal, S. , author Mandal, S.K. , et al., year 2009 . title Rational drug design . journal European journal of pharmacology volume 625 , pages 90--100

    work page 2009

  26. [26]

    , author Bojanic, D

    author Mayr, L.M. , author Bojanic, D. , year 2009 . title Novel trends in high-throughput screening . journal Current opinion in pharmacology volume 9 , pages 580--588

    work page 2009

  27. [27]

    , author Engel, J.H

    author Mittal, G. , author Engel, J.H. , author Hawthorne, C. , author Simon, I. , year 2021 . title Symbolic music generation with diffusion models , in: editor Lee, J.H. , editor Lerch, A. , editor Duan, Z. , editor Nam, J. , editor Rao, P. , editor van Kranenburg, P. , editor Srinivasamurthy, A. (Eds.), booktitle Proceedings of the 22nd International S...

    work page 2021

  28. [28]

    , author Dhariwal, P

    author Nichol, A.Q. , author Dhariwal, P. , author Ramesh, A. , author Shyam, P. , author Mishkin, P. , author Mcgrew, B. , author Sutskever, I. , author Chen, M. , year 2022 . title Glide: Towards photorealistic image generation and editing with text-guided diffusion models , in: booktitle International Conference on Machine Learning , organization PMLR ...

    work page 2022

  29. [29]

    , author Li, M

    author Ning, M. , author Li, M. , author Su, J. , author Salah, A.A. , author Ertugrul, I.O. , year 2024 . title Elucidating the exposure bias in diffusion models . https://arxiv.org/abs/2308.15321, arXiv:2308.15321 http://arxiv.org/abs/2308.15321

    work page arXiv 2024

  30. [30]

    , author Sangineto, E

    author Ning, M. , author Sangineto, E. , author Porrello, A. , author Calderara, S. , author Cucchiara, R. , year 2023 . title Input perturbation reduces exposure bias in diffusion models , in: booktitle International Conference on Machine Learning , organization PMLR . pp. pages 26245--26265

    work page 2023

  31. [31]

    , author Banck, M

    author O'Boyle, N.M. , author Banck, M. , author James, C.A. , author Morley, C. , author Vandermeersch, T. , author Hutchison, G.R. , year 2011 . title Open babel: An open chemical toolbox . journal Journal of cheminformatics volume 3 , pages 1--14

    work page 2011

  32. [32]

    , author Luo, S

    author Peng, X. , author Luo, S. , author Guan, J. , author Xie, Q. , author Peng, J. , author Ma, J. , year 2022 . title Pocket2mol: Efficient molecular sampling based on 3d protein pockets , in: booktitle International Conference on Machine Learning , organization PMLR . pp. pages 17644--17655

    work page 2022

  33. [33]

    , author Masuda, T

    author Ragoza, M. , author Masuda, T. , author Koes, D.R. , year 2022 a. title Generating 3d molecules conditional on receptor binding sites with deep generative models . journal Chemical science volume 13 , pages 2701--2713

    work page 2022

  34. [34]

    , author Masuda, T

    author Ragoza, M. , author Masuda, T. , author Koes, D.R. , year 2022 b. title Generating 3d molecules conditional on receptor binding sites with deep generative models . journal Chemical science volume 13 , pages 2701--2713

    work page 2022

  35. [35]

    , author Seward, C

    author Rasul, K. , author Seward, C. , author Schuster, I. , author Vollgraf, R. , year 2021 . title Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting , in: booktitle International Conference on Machine Learning , organization PMLR . pp. pages 8857--8868

    work page 2021

  36. [36]

    , year 2007

    author Rognan, D. , year 2007 . title Chemogenomic approaches to rational drug design . journal British journal of pharmacology volume 152 , pages 38--52

    work page 2007

  37. [37]

    , author Hoogeboom, E

    author Satorras, V.G. , author Hoogeboom, E. , author Welling, M. , year 2021 . title E (n) equivariant graph neural networks , in: booktitle International conference on machine learning , organization PMLR . pp. pages 9323--9332

    work page 2021

  38. [38]

    , author B \'e quignon, O.J

    author Schoenmaker, L. , author B \'e quignon, O.J. , author Jespers, W. , author van Westen, G.J. , year 2023 . title Uncorrupt smiles: a novel approach to de novo design . journal Journal of Cheminformatics volume 15 , pages 22

    work page 2023

  39. [39]

    , year 2005

    author Sneader, W. , year 2005 . title Drug discovery: a history . publisher John Wiley & Sons

    work page 2005

  40. [40]

    , author Weiss, E

    author Sohl-Dickstein, J. , author Weiss, E. , author Maheswaranathan, N. , author Ganguli, S. , year 2015 . title Deep unsupervised learning using nonequilibrium thermodynamics , in: booktitle International conference on machine learning , organization PMLR . pp. pages 2256--2265

    work page 2015

  41. [41]

    , author Ermon, S

    author Song, Y. , author Ermon, S. , year 2019 . title Generative modeling by estimating gradients of the data distribution . journal Advances in neural information processing systems volume 32

    work page 2019

  42. [42]

    , author Sohl-Dickstein, J

    author Song, Y. , author Sohl-Dickstein, J. , author Kingma, D.P. , author Kumar, A. , author Ermon, S. , author Poole, B. , year 2021 . title Score-based generative modeling through stochastic differential equations , in: booktitle International Conference on Learning Representations . https://openreview.net/forum?id=PxTIG12RRHS

    work page 2021

  43. [43]

    , author Barzilay, R

    author Walters, W.P. , author Barzilay, R. , year 2020 . title Applications of deep learning in molecule generation and molecular property prediction . journal Accounts of chemical research volume 54 , pages 263--270

    work page 2020

  44. [44]

    , year 1988

    author Weininger, D. , year 1988 . title Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules . journal Journal of chemical information and computer sciences volume 28 , pages 31--36

    work page 1988

  45. [45]

    , year 1997

    author White, N. , year 1997 . title Assessment of the pharmacodynamic properties of antimalarial drugs in vivo . journal Antimicrobial agents and chemotherapy volume 41 , pages 1413--1422

    work page 1997

  46. [46]

    , author Yu, L

    author Xu, M. , author Yu, L. , author Song, Y. , author Shi, C. , author Ermon, S. , author Tang, J. , year 2021 . title Geodiff: A geometric diffusion model for molecular conformation generation , in: booktitle International Conference on Learning Representations

    work page 2021

  47. [47]

    , author Cui, Y

    author Yang, L. , author Cui, Y. , author Xuan, Y. , author Wang, C. , author Belongie, S. , author Estrin, D. , year 2018 . title Unbiased offline recommender evaluation for missing-not-at-random implicit feedback , in: booktitle Proceedings of the 12th ACM conference on recommender systems , pp. pages 279--287

    work page 2018

  48. [48]

    , author Feng, Y

    author Zhang, W. , author Feng, Y. , author Meng, F. , author You, D. , author Liu, Q. , year 2019 . title Bridging the gap between training and inference for neural machine translation , in: editor Korhonen, A. , editor Traum, D. , editor M \`a rquez, L. (Eds.), booktitle Proceedings of the 57th Annual Meeting of the Association for Computational Linguis...

    work page doi:10.18653/v1/p19-1426 2019