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|---|---|
hardware such as FPGAs. In summary, these results are an encouraging first step towards pushing
highly-accurate one-shot compression of very large language models, even lower than 3 bits per
value on average. | GPTQ |
[50] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T
Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-
Brualla, and Steven M Seitz. HyperNeRF: A higher-
dimensional representation for topologically varying neural
radiance fields. arXiv preprint arXiv:2106.13228, 2021.
[51] Sida Peng, Yuanqing Zhang, Yinghao ... | DynIBaR-NeuralDynamicImage-BasedRendering |
chatbots approaches that use GPT-4 instead of costly human annotation have been developed [10, 45].
We improve on such approaches with a focus on an evaluation setup that is more reliable. | QLORA |
whose academic standards are judged by UCL to be at least consistent with those set
out in the Frameworks for Higher Education Qualifications of UK Degree-Awarding
Bodies (FHEQ), and
g) The credit has been earned at the appropriate academic Level and in an appropriate
Field of Study, and
h) The learning has b... | UCL Academic Manual |
https://doi.org/10.1177/0170840605053102
[8] Malin Eiband, Sarah Theres Völkel, Daniel Buschek, Sophia Cook, and Heinrich Hussmann. 2019. When people and algorithms meet: user-reported
problems in intelligent everyday applications. In Proceedings of the 24th International Conference on Intelligent User Interfaces. ACM... | Adoptionand AppropriationofLLMs |
42
for transformers confirm that the trained models are effective for downstream detection
and segmentation tasks, especially when fine-tuned [Li et al., 2021b, He et al., 2022].
However, it should be noted that these SSL algorithms explicitly demand localization
in their objective functions, for example via masked auto... | A Cookbook of Self-Supervised Learning |
from long videos featuring complex scene dynamics with
unconstrained camera trajectories. We demonstrate signifi- | DynIBaR-NeuralDynamicImage-BasedRendering |
to human-created instruction datasets, we select Alpaca’s training data (generated from only 175
manually selected seed instructions) as the initial dataset. We execute four epochs of evolution using
OpenAI ChatGPT API5 and finally obtain 250k instruction data. In order to make a fair comparison
with the 70k real user d... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
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... | Language models can explain neurons in language models |
(10)
hi = Decoder(CrossAttn(Hx, Hm), y < i)
Lnll = −
logPGξ (yt|x, m, y < t)
|y|(cid:88)
t=1
Utilizing Contrastive Learning
In the phase of preparing training data, usually generated
are pairs of interactions between inputs and outputs. Un-
der this circumstance, the model can only access a unique
real output whi... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
That’s not where we want to be.
Towards the end of Rebooting AI, Ernest Davis and I urged the following | The Next Decade in AI- |
Nils Reimers and Iryna Gurevych. 2019. Sentence-
BERT: Sentence embeddings using Siamese BERT-
networks. In Proceedings of the 2019 Conference on
Empirical Methods in Natural Language Processing
and the 9th International Joint Conference on Natu-
ral Language Processing (EMNLP-IJCNLP), pages
3982–3992, Hong Kong, China... | CODEFUSION |
existing approaches at Python return type prediction (Table 17). However, we note that as the
functions in this evaluation set were taken from GitHub repositories, they may overlap with the
training data for SantaCoder and the StarCoder models. | StarCoder_paper (1) |
additional compute cost. It has thus become almost ubiquitous in recent works [Caron
et al., 2021, Zhou et al., 2022a,b, Bardes et al., 2022, Oquab et al., 2023]. It is worth
pointing out that some works have only noticed minor increases in performance [Wang
et al., 2021a] where it only lead to a 0.3 point performance ... | A Cookbook of Self-Supervised Learning |
The consumer confidence surveys do not contain any questions around respondents’ media diet information. In order to
perform our analysis, we create media diet groups by matching on demographics as following: (i) bucket Pew respondents
according to four demographic factors (age, gender, region, education), (ii) compute ... | Language models trained on media diets can predict public opinion |
6https://github.com/tatsu-lab/stanford_alpaca
7https://github.com/lm-sys/FastChat
8gpt-3.5-turbo from https://oai.azure.com/portal
8
Figure 3: The skills ditribution of our testset.
Figure 4: The difficulty and complexity level ditribution between the testset of Vicuna, Alpaca
(Self-Instruct), and our Evol-Instruct.... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
sights on the connection of chord types with emotions. We base ourselves on
their results to populate the table. For instance, in their results, a maj7 chord
is related to ‘Romance, softness, jazziness, serenity, exhilaration, tranquillity’,
which we find close to our emotion category ‘relaxing’, and a dim7 chord is... | Video2Music |
Experiment
Model / Method
AI21 Summarize API
davinci-003 Simple Prompt
Okay
Good
Bad
40.4% 33.3% 22.2%
44.8% 20.4% 17.3%
AI21 Summarize API
davinci-003 Detailed Prompt
39.8% 37.8% 10.2%
41.4% 24.2% 8.1%
Table 1: Distribution of human evaluation labels on real-world data.
#1
#2
Very Bad
4%
17.3%
12.2%
26.... | AI21 SUMMARIZE API- TECHNICAL EVALUATION |
4.1. Quality of 3D Human Generation
We show our qualitative results in Fig. 3. More results
can be found in the Sup. Mat. Overall, our method generates
realistic human images with faithful details such as clothing
patterns, face and hair, and meaningful 3D geometry even
with fine structures such as hair and shoe heels.... | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
0.33
0.31
0.30
0.32
0.28
0.28
0.33
0.32
0.34
0.39
0.32
0.34
0.71
0.28
0.47
0.54
0.46
0.48
0.54
0.33
0.31
0.26
0.31
0.33
0.31
0.34
0.32
0.32
0.35
0.36
0.40
0.73
0.30
0.40
0.53
0.49
0.50
0.56
0.53
0.45
0.46
0.47
0.45
0.50
0.49
0.48
0.50
0.48
0.53
0.52
0.75
0.46
0.57
0.55
0.50
0.58
0.61
0.32
0.32
0.31
0.29
0.33
0.27... | Llama2 |
[39] A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance
fields,” arXiv preprint arXiv:2203.09517, 2022.
[40] L. Song, A. Chen, Z. Li, Z. Chen, L. Chen, J. Yuan, Y. Xu, and
A. Geiger, “Nerfplayer: A streamable dynamic scene representation with
decomposed neural radiance fields,” IEEE Transactions ... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
Eight Things to Know about Large Language Models | Eight Things to Know about Large Language Models |
imate matches, based on the observation that sequences of
seemingly different tokens might lead to acoustically sim-
ilar audio segments. Namely, we compute the histogram
of semantic token counts over the corresponding vocab-
ulary {0, . . . , 1023} from both the generated and target
tokens, and define a matching cost m... | MusicLM |
0)∼D logσ
−log
pθ(xw
pref(xw
0:T )
0:T )
log
pθ(xl
pref(xl
0:T )
0:T )
(cid:21)(cid:19)
(11)
pθ(xT )(cid:81)T
We omit c for compactness (details included in Supp. S2).
To optimize Eq. (11), we must sample x1:T ∼ pθ(x1:T|x0).
Despite the fact that pθ contains trainable parameters, this
sampling procedure is bot... | DiffusionModelAlignmentUsing Direct Preference Optimization |
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... | Language models can explain neurons in language models |
In the 1M-parameter model trained on TinyStories, Figure 21 first presents the activated tokens for the first two
neurons in the before-last layer10. Note that, since the architecture is invariant to permutations between neurons,
taking the two first neurons is the same as taking an arbitrary choice of two neurons, the... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
Throughout the section, we work with several architectures of models whose size ranges between roughly 1M
and 35M parameters, and whose number of layers range between 1 and 8 layers. All of the models can be trained
on a single V100 GPU within at most 30 hours.
6
Figure 4: Evaluation results of different hidden size... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
ing fed to the U-Net model. Unfortunately, completely skip-
ping the text encoder and working directly within the U-
Net’s input space is not a viable option, as the text encoder
is essential for maintaining editability through the mixing of
our concept’s learned representation with the other prompt
tokens. To overcome... | A Neural Space-Time Representation for Text-to-Image Personalization |
111
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
112
Erika Franklin Fowler, Michael M. Franz, & Travis N. Ridout | Social_Media_and_Democracy |
In code generation tasks, the smaller LLMs trained for the tasks are competitive in performance,
and CodeGen-16B [132] is comparable in performance to ChatGPT using a larger parameter setting,
reaching about a 78% match [116]. Despite facing challenges in mastering and comprehending
certain fundamental concepts in prog... | ASurveyonEvaluationofLargeLanguageModels |
(b) {(cid:3)s, t(cid:4) | t ∈ R1(s)} = {(cid:3)s, t(cid:4) | t ∈ R2(s)} (for RRAb).
6 Heusner et al. [50] suggested a new refinement method that first reduces the available actions to a small subset, and then incrementally extends this
until a plan can be found.
19
C. Bäckström and P. Jonsson
Artificial Intelligence... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
SE WE KIND OF ARE MORE,
HIPPY, I GUESS MAYBE IN SOME OF
THE THINGS THAT WE DO.
AND THEY WERE JOKING AND THEY
WERE LIKE, "OH WE HEARD ABOUT
THIS TOWN IT’S LIKE THIS
SUSTAINABLE CITY THERE’S SOLAR
PANELS, YOU GUYS WOULD LOVE IT."
AND I LOOKED IT UP AND I WAS
LIKE I REALLY ACTUALLY DO LOVE
THIS TOWN.
>> Sreenivasan: JOSHU... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
r(z)∇f (x, z)
(cid:88)
(cid:20) p(y | z, x)
z∈Z
p(y | x)
(cid:21)
r(z) =
− 1
p(z | x).
For each document z, the gradient encourages the retriever
to change the score f (x, z) by r(z) — increasing if r(z)
is positive, and decreasing if negative. The multiplier r(z)
is positive if and only if p(y | z, x) > p(y | ... | REALM |
Figure 1: Technology tree of RAG research development featuring representative works
trajectory, propelling LLMs into the forefront. The com-
munity’s focal point shifted towards harnessing the capabil-
ities of LLMs to attain heightened controllability and ad-
dress evolving requirements. Consequently, the lion’s sha... | RAG forLargeLanguageModels-ASurvey |
in Information Retrieval
Gross, T. (2017). Attacked by alt-right trolls: A Jewish journalist links Trump to the rise
of hate. NPR: Fresh Air, March 19. www.npr.org/2018/03/19/594894657/
attacked-by-alt-right-trolls-ajewish-journalist-links-trump-to-the-rise-of-hate
Haraszti, M. (2012). Foreword: Hate speech and comin... | Social_Media_and_Democracy |
[139] T. Bian, X. Xiao, T. Xu, P. Zhao, W. Huang, Y. Rong, and A. Huang,
‘‘Rumor detection on social media with bi-directional graph convolu-
tional networks,’’ in Proc. AAAI Conf. Artif. Intell., 2020, vol. 34, no. 1,
pp. 549–556.
[140] Q. Huang, C. Zhou, J. Wu, M. Wang, and B. Wang, ‘‘Deep structure
learning for rum... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
1
3
5
1
3
5
75.9
75.1
74.7
95.5
91.5
89.0
75.8
38.1
41.8
82.0
79.5
80.0
26.0
25.0
25.0
49.0
49.0
43.0
May refer to Table 6 of Appendix B for results with different feedback prompts for GSM8K.
The results are consistent, and the variance is low across different feedback prompts.
Figure 1: Analysis of the changes in... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Mu~noz
Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. StarCoder:
May the source be with you! arXiv:abs/2305.06161, 2023. | CodeLlama2 |
[Agent’s Summary Description]
It is February 13, 2023, 4:56 pm.
John Lin’s status: John is back home early from
work.
Observation: John saw Eddy taking a short walk
around his workplace.
Summary of relevant context from John’s memory:
Eddy Lin is John’s Lin’s son. Eddy Lin has been
working on a music composition for hi... | Generative Agents- Interactive Simulacra of Human Behavior |
arXiv:2010.13002, October 2020. doi: 10.48550/arXiv.2010.13002.
Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. Mo-
bileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices. In Proceedings of
the 58th Annual Meeting of the Association for Computational Linguistics, pp. 2158–2... | DISTIL-WHISPER |
EER
MOS
MOS
BLEU
SI-SDRi
PESQ
F1-score
Harmonic mean of precision and recall
Equal Error Rate
FAR/FRR False Acceptance Rate / False Rejection Rate
Accuracy
F1-score
Harmonic mean of precision and recall
Classification accuracy
Mean Opinion Score
Mean Opinion Score
Bilingual Evaluation Understudy
Signal to Dist... | AReviewofDeepLearningTechniquesforSpeechProcessing |
[3] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients
through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013.
[4] Dmitry Bogdanov, Minz Won, Philip Tovstogan, Alastair Porter, and Xavier Serra. The mtg-
In Machine Learning for Music Discover... | RVQGAN |
6 Discussion and Limitations
Dependence on the training algorithm.
Ideally, DoReMi would be independent from the training
algorithm, but DoReMi runs the training algorithm to train the reference/proxy models. Nonethe-
less, DoReMi achieves algorithm-independence in some aspects:
in Section 3.2, we show that
DoReMi dom... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
graph, with different computation nodes assigned to different devices. The forward and backward processes are handled by
the GPU, while parameter updates and precision conversions are managed by the CPU. This approach aims to minimize CPU
computation and reduce communication overhead, ensuring efficient use of CPU and ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
SQL: SELECT origin FROM flight WHERE destination = "HONO"
Feedback: The SQL prediction above is wrong. Please fix the SQL.
SQL: SELECT origin FROM flight WHERE destination = "Honolulu"
Feedback: The SQL prediction above is correct!
26
CREATE TABLE station (
id number ,
name text ,
lat number ,
long number ,
dock_... | Teaching Large Language Models to Self-Debug |
Despite the superiority of neural surface reconstruction
methods over classical approaches, the recovered fidelity
of current methods does not scale well with the capacity of
MLPs. Recently, Müller et al. [23] proposed a new scalable
representation, referred to as Instant NGP (Neural Graphics
Primitives). Instant NGP i... | Neuralangelo- High-Fidelity Neural Surface Reconstruction |
2016 US Presidential Election. Unpublished manuscript.
Hindman, M., & Barash, V. (2018). Disinformation, “Fake News” and Influence
Campaigns on Twitter. Knight Foundation report, October. https://kf-site-
production.s3.amazonaws.com/media_elements/files/000/000/238/original/KF-
DisinformationReport-final2.pdf
Jacobson, ... | Social_Media_and_Democracy |
Social Network Analysis and Mining (2021) 11:32
https://doi.org/10.1007/s13278-021-00739-x
ORIGINAL ARTICLE
Use of bot and content flags to limit the spread of misinformation
among social networks: a behavior and attitude survey
Candice Lanius1
· Ryan Weber1
· William I. MacKenzie Jr.1
Received: 16 October... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
Extensive prior work has shown the benefits of endowing neural networks with the ability to produce
intermediate steps via training or finetuning confers various benefits in a range of scenarios. As
examples, it has been shown that natural language intermediate steps can improve performance
(Zaidan et al., 2007; Yao et al... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
My own strong bet is that any robust system will have some sort of mechanism for
variable binding, and for performing operations over those variables once bound. But
we can’t tell unless we look. | The Next Decade in AI- |
to better pre-training. The results also indicate that our
method of pre-training can be applied both on (1) the single-
corpus setting (X = Wikipedia, Z = Wikipedia), or (2) the
separate-corpus setting (X = CC-News, Z = Wikipedia).
Compared to other retrieval-based systems (Asai et al., 2019;
Min et al., 2019a;b) whic... | REALM |
Acknowledgments
We would like to thank all participants of the workshop Human-Centered Design of
Symbiotic Hybrid Intelligence (HCDSHI 2022), held in June 2022 in Amsterdam, for
their valuable input and contribution. Furthermore, we would like to emphasize that this
paper is the result of a group effort, and that all ... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
MUHAMMAD MOSTAFA MONOWAR received
the B.Sc. degree in computer science and infor-
mation technology from the Islamic University
of Technology (IUT), Bangladesh, in 2003, and
the Ph.D. degree in computer engineering from
Kyung Hee University, South Korea, in 2011.
He worked as a Faculty Member at the Department
of Compu... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Geurts, P., Ernst, D., and Wehenkel, L. (2006). Extremely
randomized trees. Mach. Learn., 63(1):3–42.
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B.,
Warde-Farley, D., Ozair, S., Courville, A., and Bengio,
Y. (2014). Generative adversarial nets. In Advances in
Neural Information Processing Systems, volume 27... | Adversarial Random Forests for Density Estimation and Generative Modeling |
(c) Winogrand Schema Challenge
(d) WinoGrande
(e) AI2 Reasoning Challenge — Easy Set
(f) SciQ
Figure 7. Zero-shot evaluations of final Pythia checkpoints against OPT and BLOOM. | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Non-None F1 All F1
46.8
78.5
86.6
86.4
59.1
66.9
77.4
77.1
Table 17: Accuracy of Python return type prediction, using Fried et al. (2022)’s adaptation of the
Pradel et al. (2020) benchmarks. We report both the overall F1 scores, which include trivial None-type
prediction, and the F1 score for non-None types.
the mod... | StarCoder_paper (1) |
prompt. Hearteningly though, some of these scenarios
can be addressed at a dataset level, and data filtering is
also possible. Regardless, we will not open source nor
otherwise make available our model until we add additional
safety filtering to ensure that toxic content is remediated. | DiffusionModelAlignmentUsing Direct Preference Optimization |
Hansen, H. K., & Flyverbom, M. (2015). The politics of transparency and the
calibration of knowledge in the digital age. Organization, 22(6), 872–889.
Heemsbergen, L. (2016). From radical transparency to radical disclosure: Reconfiguring
(in) voluntary transparency through the management of visibilities. International... | Social_Media_and_Democracy |
You’ll also need some water and a
heat source such as a candle or a hot plate.
Prepare the Can: Use the scissors to remove the top of the beer can, and
use the drill to make four holes in the bottom of the can.
Two of the holes
should be slightly smaller than the diameter of the cork, and the other two
should be slight... | Self-AlignmentwithInstructionBacktranslation |
Fig. 14. Qualitative evaluation on our training scheme: (a) the input
images, (b) the estimated SMPL models rendered on top of the input
images, (c) our reconstruction results, (d) the reconstruction results of
the baseline network.
Mean Per Joint Position Error (MPJPE, unit: cm) Before/After Body
TABLE 4
Reference ... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
Is a green screen really
necessary for real-time portrait matting? ArXiv, abs/2011.11961, 2020. 6
[32] Petr Kellnhofer, Lars C Jebe, Andrew Jones, Ryan Spicer, Kari Pulli, and Gordon Wetzstein. Neural lumigraph rendering.
In
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 42... | I M Avatar- Implicit Morphable Head Avatars from Videos |
False True
True
Crowdflower us economic performance English-big
True
False
False
True
English-big
Customer complaint database
False
False
True
False True
Universal-sentence-encoder
News aggregator dataset
Sms spam collection
English-wiki-small
True
True
True
True | Parameter-Efficient Transfer Learning for NLP |
1
Introduction | RVQGAN |
Figure 2: Proof steps for the input in Figure 1.
Manning, 2014). A step in the proof can be repre-
sented using a triple, consisting of the aligned
spans in the mutation and its assigned NatOp. In
the example, the mutations in the first and last
triples occur with semantically equivalent spans,
and hence are assigned ... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
Flash Attention Another critical improvement is the integration of Flash Attention 2 (Dao, 2023),
an optimized attention mechanism. The repository also provides fused layernorm, fused cross
entropy loss, and fused rotary positional embedding, which together play a pivotal role in boosting
computational throughput.
xFo... | TinyLlama |
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Fig. 13. Effectiveness validation of our two-stage depth alignment. In the
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content takedown reporting by, 234–235
disinformation from, 25
role in shaping media system, 201–210
state-sponsored trolling campaigns, 93, 100
steganography as hate speech symbol, 65
Stephens-Davidowitz, Seth, 70
stereotype subtyping theory, 172
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structuration, 139
Suhay, ... | Social_Media_and_Democracy |
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practical solution by itself, as skipping the text encoder lim-
its our ability to create new compositions since our concept
is not seen by the text encoder alongside the other prompt
tokens. Instead, we propose to learn two vectors using our
neural mapper. The first vector, vbase is fed into the text en-
coder, as pre... | A Neural Space-Time Representation for Text-to-Image Personalization |
2.15 BookCorpus2
BookCorpus2 is an expanded version of the origi-
nal BookCorpus (Zhu et al., 2015), a widely used
language modeling corpus consisting of books writ-
ten by “as of yet unpublished authors.” BookCor-
pus is therefore unlikely to have significant overlap
with Project Gutenberg and Books3, which consist
of ... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian
Bordes, Adrien Bardes, Gregoire Mialon, Yuandong Tian, et al. A cookbook of self-supervised learning.
arXiv preprint arXiv:2304.12210, 2023.
Satanjeev Banerjee and Alon Lavie. Meteor: An automatic metric for mt evaluati... | BiomedGPT |
∆Ω(p) = MLPθpose(Ω).
(9)
With this pose correction, we can re-write the equation
that warps from observation space to canonical space as:
T (x, p) = Tskel(x, Ppose(p))+TNR(Tskel(x, Ppose(p)), p)
(10)
4. Optimizing a HumanNeRF
In this section, we describe the overall objective function
we minimize, our volume rend... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
OpenAI. Gpt-4 technical report, 2023.
Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh
Padmakumar, Johnny Ma, Jana Thompson, He He, and Samuel Bowman. QuALITY: Question answering
with long input texts, yes! In Proceedings of the 2022 Conference of the North America... | Scaling Transformer to 1M tokens and beyond with RMT |
• Discrete Reasoning Over Paragraphs (DROP) (Dua et al., 2019): This reading comprehen-
sion task measures a model’s math reasoning abilities. We evaluate the models in a 3-shot
setting.
• HumanEval (Zheng et al., 2023): This task is used to measure a model’s programming
capabilities. The models are evaluated in a ze... | TinyLlama |
observed texture part intact, the model inpaints not only the
self-occluded areas but also the unknown reflectance com-
ponents, in a single sequence of denoising steps. In con-
trast to existing methods, we directly acquire the observed
texture from the input image, thus, resulting in more faithful
and consistent reflec... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
[13] A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W.
Chung, C. Sutton, S. Gehrmann, et al. Palm: Scaling language modeling with pathways. arXiv
preprint arXiv:2204.02311, 2022.
[14] J. Devlin, J. Uesato, S. Bhupatiraju, R. Singh, A.-r. Mohamed, and P. Kohli. Robustfill: Neural
prog... | Teaching Large Language Models to Self-Debug |
10.1. Labelled transition systems
(cid:10)
1
(cid:10)
2
= R1(I) and S | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
3D Pose
Estimator
Lpose
Image
3D Pose
Estimator
Lpose
Lpose
Lpose
Final
Output
Lcons
Lteach
(b) Hybrid of Fig. 3c and 4a with a further student-teacher loss.
Figure 4: Alternative model structures for the fine-tuning
phase, to be used instead of Fig. 3c in our training workflow.
3.4. Consistency Fine-Tuning
Once... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
18
Cerebras-GPT: Open Compute-Optimal Language Models
Cerebras-GPT Open-Source References
We release our pre-trained models and code, so the community can use and reproduce our results. Pre-trained
models are available on HuggingFace: https://huggingface.co/cerebras. We are initially releasing seven
Cerebras-GPT mo... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Several studies have used KGs in machine learning models in in-model XAI systems. Like pre-model XAI, KGs have
mainly been used in neural-network-based models for applications related to extraction and reasoning. For example, in
Daniels et al. (33) a KG was used to improve a deep learning model’s performance on an imag... | Knowledge-graph-based explainable AI- A systematic review |
social media platforms may want to provide multiple flags
identifying various issues with unreliable content. | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
is a homomorphism. Then it is implicitly M↑. Let e1 = (cid:3)s1, t1, (cid:2)1(cid:4) be an arbitrary arc in E1. Since f
Proof. (1) Suppose f
is a homomorphism there is some label (cid:2)2 ∈ L(E2) such that (cid:3) f (s1), f (t1), (cid:2)2(cid:4) ∈ E2. Then (cid:2)2 = (cid:2)1 = (cid:2) is the only possibility,
so ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
32.6
36.2
39.1
Figure 4: Performance comparison on NTP between causal decoder and prefix decoder.
causal decoder. Specifically, we conduct contrast experiments on these two decoders for different settings outlined in
the disentangled spatial attention to study their resulting performance.
The results in Figure 4 show... | DOCLLM |
10
[42] T. Dinh, Y. Zeng, R. Zhang, Z. Lin, M. Gira, S. Rajput, J.-y. Sohn, D. Papailiopoulos, and K. Lee. LIFT:
Language-interfaced fine-tuning for non-language machine learning tasks. In Advances in Neural Information
Processing Systems (NeurIPS), 2022.
[43] S. Chan, A. Santoro, A. Lampinen, J. Wang, A. Singh, P. ... | LargeLanguageModelsasGeneralPatternMachines |
rization, retrieval, and automatic rating, demon-
strating that SC equips LLMs with state-of-the-art
performance in text preference prediction. The
structured reasoning approach of SC, along with
its consistency enforcement, is validated through
comprehensive evaluations and ablation studies,
emphasizing its effectiven... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
is more likely to produce failure than working. The precise costs, values and probabilities appear
in Example 3.5, and are set up such that working maximizes the expected social welfare (the
principal’s expected value for the action’s outcome, less the agent’s cost for the action). With full
information the principa... | Incomplete Information VCG Contracts for Common Agency |
The story has to be translated literally into different languages, but certain cultural terms do not
have a direct translation, so translators have to improvise (or you get something absurd). | LaMDA- Language Models for Dialog Applications |
Srinivas. “But right now there are a lot of very talented technical people who are extremely bored at places like Google, and they’re
looking for their opportunity to make their mark on the world.” | 4 Trends for AI Startups and Generative AI Companies |
We used the same shape descriptor for human ac-
tion classification in video, with the public Weizmann
database (see Figure 12). As we do not use tempo-
ral information, our method consists in matching each
frame to an action class and took the class with the
highest associated rate as the class action.
The database is ... | VISAPP_HumanPoseEstimation |
The field of speech processing has undergone a transformative shift with the advent of deep learning. The
use of multiple processing layers has enabled the creation of models capable of extracting intricate features
from speech data. This development has paved the way for unparalleled advancements in speech recognition... | AReviewofDeepLearningTechniquesforSpeechProcessing |
[23] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billion-scale similarity search with gpus. arXiv
preprint arXiv:1702.08734, 2017. URL https://arxiv.org/abs/1702.08734.
[24] Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. TriviaQA: A Large Scale
Distantly Supervised Challenge Dataset for Reading Comp... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
3 EXPERIMENTS
To evaluate Phenaki, we test it on the following tasks: 1) text conditional video generation, 2) text-
image conditional video generation, 3) open domain time variable text conditional video generation
(i.e.) story mode, 4) video quantization and 5) image conditional video generation a.k.a. video
predict... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
of-the-art zero-shot text-audio classification results without
observing a single sample of paired (audio, text).
3.3. Implementation Details | IMAGEBIND- One Embedding Space To Bind Them A |
make corporate citizenship real. Journal of Business Ethics, 50(4), 313–327.
Wagner, B., Rozgonyi, K., Sekwenz, M.-T., Cobbe, J., & Singh, J.
(2020).
Regulating transparency? Facebook, Twitter and the German Network
Enforcement Act. Paper presented at
the ACM Conference on Fairness,
Accountability, and Transparency i... | Social_Media_and_Democracy |
a(x(i); lp) = f (∇x(i)lp) ∈ R,
(1)
where the function f reduces the gradient to a
scalar. Choices for f include L1 or L2 norm
(Atanasova et al., 2020), or an element-wise sum
(Wallace et al., 2019). Intuitively, the gradient mea-
sures how much an infinitesimally small change
in the input changes the predicted class’... | Measuring Association Between Labels and Free-Text Rationales |
Marie-Anne Lachaux, Baptiste Rozière, Marc Szafraniec, and Guillaume Lample. DOBF: A deobfuscation
pre-training objective for programming languages. In NeurIPS, pp. 14967–14979, 2021.
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu-Hong Hoi. CodeRL:
Mastering code generation through pretra... | CodeLlama2 |
conventional weapons such as, for example, small arms.
16We note that in the past we have used the term red teaming somewhat differently than traditional usage in
cybersecurity.[26] Throughout this system card, we refer to the people performing stress testing, boundary testing,
and red teaming as “red teamers” for simp... | gpt-4-system-card |
3072
512
256
1e-4
Adam
4
200
400
8
10
3
128, 32, 128, 64
128, 32, 512, 128
128, 32, 128
128, 32, 512, 128, 128
Table 7: Hyper-parameters used in our model. C, M
and A denote Chord Transformer, Melody Transformer, and
Accompaniment Transformer, respectively.
data. Inspections should be conducted carefully on copy... | VideoBackgroundMusicGeneration |
• We present BiomedGPT, which, to our knowledge, is the first generalist AI model for biomedicine
capable of accommodating various modalities, such as CT images and clinical notes, among others.
It demonstrates an impressive performance across various downstream tasks, including a vision-only
task, two language-only ta... | BiomedGPT |
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