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[18] Enkelejda Kasneci, Kathrin Sessler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan
Günnemann, Eyke Hüllermeier, Stephan Krusche, Gitta Kutyniok, Tilman Michaeli, Claudia Nerdel, Jürgen Pfeffer, Oleksandra Poquet, Michael
Sailer, Albrecht Schmidt, Tina Seidel, Mat... | Adoptionand AppropriationofLLMs |
10
videos. V2Meow utilizes pretrained visual features extracted from silent video
clips to generate music audio waveforms. In addition, it provides control over the
music style through supporting text prompts alongside video frame conditioning.
V2Meow uses audio waveforms as the training input and output instead o... | Video2Music |
Input Canny edgeDefault“masterpiece of fairy tale, giant deer, golden antlers”Input human poseDefault“chef in kitchen”“…, quaint city Galic”“Lincoln statue” the mapping from conditioning images to target images. The
research community has also taken steps to control text-
to-image models with spatial masks [6, 20], ima... | AddingConditionalControltoText-to-ImageDiffusionModels |
Figure 5. Accuracy on Trivia QA plotted againts the number of relevant entity counts found in a QA-pair. Each subfigure shows the
impact of performance across various model sizes over multiple intermediate checkpoints. Each point represents the average accuracy
(y-axis) of binned counts (x-axis).
Bach, S. H., Sanh, V.,... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Gemini: A Family of Highly Capable Multimodal Models
Contributors
Ofir Roval
Reiko Tojo
Michael Kwong
James Lee-Thorp
Christopher Yew
Quan Yuan
Sumit Bagri
Danila Sinopalnikov
Sabela Ramos
John Mellor
Abhishek Sharma
Aliaksei Severyn
Jonathan Lai
Kathy Wu
Nanxin Chen
Heng-Tze Cheng
David Miller
Nicolas Sonnerat
Denis ... | gemini_1_report |
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the dangers of
stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness,
accountability, and transparency, pages 610–623, 2021b. | Llama2 |
[577] Reeves, B., C. Nass. The media equation - how people treat computers, television, and new
media like real people and places. Cambridge University Press, 1996.
[578] Roose, K. A conversation with bing’s chatbot left me deeply unsettled, 2023.
[579] Li, K., A. K. Hopkins, D. Bau, et al. Emergent world representa... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
forecasting environments.[93]
19
claims or implications—including that it is human—and to consider the potential impact of changes
to the model’s style, tone, or perceived personality on users. We also suggest that developers
communicate to users the importance of critically evaluating model outputs.
At the model-l... | gpt-4-system-card |
corpus.. In LREC. 125–129.
[469] Sidheswar Routray and Qirong Mao. 2022. Phase sensitive masking-based single channel speech enhancement using
conditional generative adversarial network. Computer Speech & Language 71 (2022), 101270.
[470] Mickael Rouvier, Richard Dufour, and Pierre-Michel Bousquet. 2021. Review of d... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Revolutionary War, from 1775 to 1803, and it marked the beginning of the end of slavery
and the end of democracy. - The Civil War is characterized by the draft, which led to the
expansion of the military and the a | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
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D-Adaptation, and whether it can scale to the largest model. The convergence for both methods can
be observed on the train and validation set in Figure A.4. | Simple and Controllable Music Generation |
for those instructions. They further perform manually engineered filtering rules to remove low-quality
instruction-output pairs. Xu et al. [2023] generates more complex instructions by creating variants of
user instructions sent to ChatGPT.
All these approaches use model-generated responses for training data. More simi... | Self-AlignmentwithInstructionBacktranslation |
Qualitative evaluation: Samples from this model can be seen in Figure 3 and additional samples
are provided at phenaki.github.io. We observe that there is a high degree of control over both the
actors and the background dynamics in the videos. The appearance of the actors and the video style
can be adjusted by the text... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
Finally, we observed the possible effect of instruction tuning [78],
which seemed to guide the behavior of the agents to be more polite
and cooperative overall. As noted earlier in the paper, the dialogue
generated by the agents could feel overly formal, as seen in Mei’s
conversations with her husband John, where she o... | Generative Agents- Interactive Simulacra of Human Behavior |
Music is essential when editing videos, but selecting mu-
sic manually is difficult and time-consuming. Thus, we seek
to automatically generate background music tracks given
video input. This is a challenging task since it requires
music-video datasets, efficient architectures for video-to-
music generation, and reason... | VideoBackgroundMusicGeneration |
https://www.pewresearch.org/internet/2022/03/17/ai-and-human-enhancement-americans-openness-is-tempered-by-a-range-of-concerns/
4/12
21/11/2023, 11:57
AI and Human Enhancement: Americans’ Openness Is Tempered by a Range of Concerns | Pew Research Center
Sharp partisan divisions anchor people’s views about possible... | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
Results on HPO-B. As shown in Table 2, MLCopilot, highlighted in gray, achieved the highest
normalized accuracy (nAcc) across all three trials. The improvement is particularly significant for
the first attempt (nAcc@1). It is remarkable that LLM-FS has already surpassed all the traditional
baselines, suggesting the large... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Inventory (28/36) : {’ rail ’: 1, ’coal ’: 2, ’ oak_planks ’: 13 , ’
copper_block ’: 1, ’diorite ’: 7, ’ cooked_beef ’: 4, ’granite ’: 22 , ’
cobbled_deepslate ’: 23 , ’feather ’: 4, ’leather ’: 2, ’
cooked_chicken ’: 3, ’ white_wool ’: 2, ’stick ’: 3, ’ black_wool ’: 1,
’ stone_sword ’: 2, ’ stone_hoe ’: 1, ’ stone_a... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
within hours on a single RTX 4090. All these PFT methods can be helpful either for fine-tuning a model to a specific
task or tuning LLMs to meet special requirements like human alignment. | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
-0.35
-0.19
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Figure 6: Breakdown of the memory footprint of different LLaMA models. The input gradient size is for batch
size 1 and sequence length 512 and is estimated only for adapters and the base model weights (no attention).
Numbe... | QLORA |
30290540 (visited on 04/29/2022).
Paul Christiano. What failure looks like - AI Alignment Forum. URL: https://www.
alignmentforum.org/posts/HBxe6wdjxK239zajf/what-failure-looks-like (visited on
04/29/2022).
Paul F Christiano et al. “Deep Reinforcement Learning from Human Prefer-
ences”. In: Advances in Neural Informati... | Is Power-Seeking AI an Existential Risk? |
et al. [38], Holte and Choueiry [57] or Zucker [93], and to the book by Saitta and Zucker [80] for a broader and more
extensive treatment of the topic. | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
presentation, we only discuss how to compute F (x) – the values G(x) can be computed analogously.4
Before proceeding with a formal argument, we give a high-level explanation of the acceleration. In
practice, we only need to evaluate a small fraction of PC units to compute each of its D marginals. | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
Student Recruitment & Admissionswww.ed.ac.uk/student-recruitment 10
Training and supervision
The training and supervision of research students is an important consideration. Prospective postgraduate research
students will be expected to gain specialist and transferable skills so, if the funder requires it, indicate w... | research proposal guidance |
Conneau et al., 2017; Cer et al., 2019).
To encode context in these representations, features are
extracted from internal representations of sequence models,
such as MT systems (McCann et al., 2017), and BiLSTM
language models, as used in ELMo (Peters et al., 2018). As
with adapters, ELMo exploits the layers other than... | Parameter-Efficient Transfer Learning for NLP |
model notion of likelihood, utilizing the evidence lower
bound to derive a differentiable objective. Using the Pick-
a-Pic dataset of 851K crowdsourced pairwise preferences,
we fine-tune the base model of the state-of-the-art Stable
Diffusion XL (SDXL)-1.0 model with Diffusion-DPO. Our
fine-tuned base model significant... | DiffusionModelAlignmentUsing Direct Preference Optimization |
t αt(1 − ¯αt)
3.3 Data scaling, reverse process decoder, and L0
We assume that image data consists of integers in {0, 1, . . . , 255} scaled linearly to [−1, 1]. This
ensures that the neural network reverse process operates on consistently scaled inputs starting from
the standard normal prior p(xT ). To obtain discret... | Denoising Diffusion Probabilistic Models |
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division
How much is {x} over {y}?
What is {x} over {y}?
0
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4
Table 10: Question formats for single-operation problems. Five different formats
for each type of operation in the case of single-operation expressions.
The equations for two-operation problems, for all combinations of placing the
brackets,... | MRKL Systems |
IJCNLP - Findings, pages 968–988.
Daniel Fried, Ronghang Hu, Volkan Cirik, Anna
Rohrbach, Jacob Andreas, Louis-Philippe Morency,
Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein,
and Trevor Darrell. 2018. Speaker-follower models
for vision-and-language navigation. In Advances in
Neural Information Processing Systems (Ne... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
6.2 Conditions
All conditions were used to independently answer each of the inter-
view questions. We compared the generative agent architecture to
ablations that disabled the agents’ access to some of all of its three
types of memory in its memory stream—observation, reflection, and
planning—and to a human-generated c... | Generative Agents- Interactive Simulacra of Human Behavior |
Pierre Chambon, Christian Bluethgen, Curtis P Langlotz, and Akshay Chaudhari. Adapting pretrained
vision-language foundational models to medical imaging domains. arXiv preprint arXiv:2210.04133, 2022.
13
Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, and Diyi Yang. Parameter-efficient fine-tuning
In The E... | BiomedGPT |
It has also been increasingly common practice
to combine multiple datasets when training lan-
guage models. For instance, GPT (Radford et al.,
2018) was trained on Wikipedia and BookCorpus,
whereas GPT-3 (Brown et al., 2020) was trained
on Wikipedia, two fiction datasets, and two web-
scraped datasets. The Pile continue... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Question: What is the total number of hours per week and number of games
played by students under 20?
Answer: "What is the total number of hours per week" returns 1 column. "What
is the number of games played" returns 1 column. "What is the total number
of hours per week and number of games played" returns 2 columns. T... | Teaching Large Language Models to Self-Debug |
πθ(yw|x)
πref(yw|x)
− β log
πθ(yl|x)
πref(yl|x)
∇θ log π(yw | x)−∇θ log π(yl | x)
After using the reward substitution of ˆrθ(x, y) = β log πθ(y|x)
gradient from Section 4.
πref(y|x) we obtain the final form of the
A.5 Proof of Lemma 1 and 2
In this section, we will prove the two lemmas from Section 5.
Lemma 1 Re... | Direct Preference Optimization |
es,butjustcalloneAPIinoneresponse.GenerateONLYonepieceofthoughtandonepieceofaction/answereachtime,donotgivemore!Keepyourresponsesuccinctandnomorethanoneline.Yourresponseshouldbeginwith"Thought:"or"Action:"or"Answer:"."Thought:":Generateyourthoughtaboutwhattodonext."Action:":CalloneofthetwoAPIsinacorrectformat."Answer:"... | Tool Learning with Foundation Models |
person who shared it matters more than the news organization that produced it
(American Press Institute 2017). Users are more likely to think news is accurate
and well-balanced when it is shared by someone they trust, which may
encourage the spread of misinformation, especially because platforms surface
these close fri... | Social_Media_and_Democracy |
We used the same method and rationale described above to independently shape per-
sonality in LLMs, but with modified personality profile prompts that reflect simultaneous
targeted changes in personality traits. To optimize the computational cost of this study, we
generated 32 personality profiles, representing all pos... | PersonalityTraitsinLargeLanguageModels |
nb(cid:88)
i=1
x(cid:48) =
wi Bi x
Deformer: To enable animation and to learn from posed
images, we require the appearance and 3D shape in posed
space. In the following we denote quantities in posed space
as (·)(cid:48). Given the bone transformation matrix Bi for joint
i ∈ {1, ..., nb}, a canonical point x is tran... | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
OutputhiddenhiddenInputdilation=1dilation=2dilation=4 16
Mehrish et al.
back-propagation route depends solely on the depth of the network. This makes TCNNs
a more memory-efficient alternative to LSTMs and GRUs, especially in scenarios where
memory constraints are a concern.
TCNNs can perform real-time speech enhance... | AReviewofDeepLearningTechniquesforSpeechProcessing |
QUESTION: I spilled my coke on the table, could you throw it away and then bring me something to help clean?
MODEL ANSWER (CORRECT): Explanation: The user has spilled their coke on the table. I will throw away
the coke and then bring the user a sponge. Plan: find(coke), pick(coke), find(trash), put(coke), find(sponge),
pi... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
The destructive side of creative destruction is clear to see in the decline of
legacy news media, the creative side too, with the rise of platform companies
like Google, Facebook, and Twitter, the multitude of ways in which many
different actors make use of digital technologies broadly and platform
products and service... | Social_Media_and_Democracy |
Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta.
In EMNLP, 2021. URL https://
Muppet: Massive multi-task representations with pre-finetuning.
aclanthology.org/2021.emnlp-main.468.
Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, and Sr... | Scaling Instruction-Finetuned Language Models |
162Or at least, that’s my current impression of the story re: nuclear power, cloning, and human genetic
engineering. Chemical weapons might be another example in the vicinity: my impression is that use of chemical
weapons in combat decreased in WWII, following WWI. See Pinker (2018) for some examples of dire, and false... | Is Power-Seeking AI an Existential Risk? |
[35] Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu,
Noah Snavely, and Angjoo Kanazawa. KeypointDeformer:
Unsupervised 3D keypoint discovery for shape control. In
CVPR, 2021.
[36] Xiaopeng Ji, Qi Fang, Junting Dong, Qing Shuai, Wen Jiang,
and Xiaowei Zhou. A survey on monocular 3D human pose
estimation. Virtua... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
[491] Jonathan Shen, Ruoming Pang, Ron J Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang,
Yuxuan Wang, Rj Skerrv-Ryan, et al. 2018. Natural tts synthesis by conditioning wavenet on mel spectrogram
predictions. In 2018 IEEE international conference on acoustics, speech and signal processing (... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Item Reduction. To construct a coherent and consistent initial item set based on the instruments described
3.1.8
above, the authors have put forth a set of criteria that would inform the wording and selection of the items. In
detail, it was prioritized the use of positive, unambiguous and concise phrasing, the use of d... | Society’sAttitudesTowardsHumanAugmentation |
8.2 18.2
1.3
0.5
8.3 13.7
1.2 10.1 15.0
1.4 10.6 16.8
0.2
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Table 6: Results on MLQA for Spanish (Es), German
(De), Hindi (Hi), Vietnamese (Vi), Chinese (Zh) and
Arabic (Ar). While using the machine translation tool
to translate questions is helpful across all languages,
further pretraining on CCNet... | Toolformer |
• Self-verification is the most important among all the feedback types. Removing the
module leads to a significant drop (−73%) in the discovered item count. Self-verification
serves as a critical mechanism to decide when to move on to a new task or reattempt a
previously unsuccessful task.
• GPT-4 significantly outperf... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
a memory to save all the experiences on past tasks. By re-
trieving memory entries relevant to the newly-coming task
and putting them into the context as a reference, JARVIS-1
is able to accumulate more experiences as the game contin-
ues and strengthen its own planning skills without gradient
update. As illustrated in... | JARVIS-1 |
Figure 7:
Testing accuracy
on questions with short length,
medium length and long length.
8
| METAMATH |
strikesafavorablebalancebetweensamplecomplexity,simplicity,andwall-time.1IntroductionInrecentyears,severaldifferentapproacheshavebeenproposedforreinforcementlearningwithneuralnetworkfunctionapproximators.TheleadingcontendersaredeepQ-learning[Mni+15],“vanilla”policygradientmethods[Mni+16],andtrustregion/naturalpolicygrad... | PPO |
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Low-rank Adjustment. DyLoRA (Dynamic LoRA) [44] is
introduced to overcome two limitations of LoRA: (a) LoRA’s
rank is fixed and prevents any changes after training (b)
determining the optimal rank for LoRA requires exhaustive
search and considerable effort. DyLoRA trains LoRA modules
for a range of ranks instead of a s... | Parameter-EfficientFine-TuningMethods |
from
scratch
LLM+ViT
pretrain
Single robot
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PaLI (Zero-shot) (Chen et al., 2022)
QT-OPT (Kalashnikov et al., 2018)
PaLM-E-12B
trained on
from
scr... | PaLM-E- An Embodied Multimodal Language Model |
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a set of images, often assuming the scene is rigid and
camera poses can be accurately registered to the back-
ground [11, 23, 27–29, 59]. To extend NeRF to dynamic
scenes, recent works introduce additional functions to de-
form observed points to a canonical space or over time
[22, 33, 34, 38, 53, 57]. However, they he... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
[496] Bard, N., J. N. Foerster, S. Chandar, et al. The hanabi challenge: A new frontier for ai research.
Artificial Intelligence, 280:103216, 2020.
[497] Wang, X., W. Shi, R. Kim, et al. Persuasion for good: Towards a personalized persuasive
dialogue system for social good. In A. Korhonen, D. R. Traum, L. Màrquez, ed... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
summed over synonyms over the target word. This helps address the “surface form competition” issue,30 and allows the probing
to be less sensitive to the exact question phrasing and target word choice. More details are in the Methods section. | Language models trained on media diets can predict public opinion |
8.2. Potential risks and benefits
Interpretability. One major advantage of code generation models is that code itself is relatively
interpretable. Understanding the behavior of neural networks is challenging, but the code that code
generation models output is human-readable and can be analysed by traditional methods (an... | alphacode |
50See e.g. Yudkowsky (undated): “Another concern is that consequentialism may to some extent be a
convergent or default outcome of optimizing anything hard enough. E.g., although natural selection is a
pseudoconsequentialist process, it optimized for reproductive capacity so hard that it eventually spit out some
powerf... | Is Power-Seeking AI an Existential Risk? |
Can you tell me what got the USA involved in World War II?
In the 1930s, the United States found itself at the center of a growing political and
economic crisis that threatened the world’s stability. The Great Depression had
brought many other countries to the brink, including many European ones, and
the United States ... | Direct Preference Optimization |
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot,
Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier,
Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas
Wang, Timothée Lacroix, and William El Saye... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
It is not unreasonable to think that the average human being might know (or
immediately recognize as true) millions or perhaps tens of millions of such facts; the
great majority of these must be learned, whether through experience, or explicit
instruction, or other means. Importantly, virtually any of that knowledge... | The Next Decade in AI- |
During training we use dropout (Srivastava et al., 2014) with probability 0.1. The training is run
for a maximum of 20000 training steps with a patience of 1600 steps and validation frequency of
200. We use a batch size of 1000 words. For the optimizer we use AdamW (Loshchilov and Hutter,
2018) with β1 = 0.9 and β2 = 0... | MULTI HASH EMBEDDINGS IN SPACY |
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang,
Xiao Chen, Linlin Li, Fang Wang, and Qun Liu.
2020. TinyBERT: Distilling BERT for natural lan-
guage understanding. In Findings of the Association
for Computational Linguistics: EMNLP 2020, pages
4163–4174, Online. Association for Computational
Linguistics.
Jared Kapla... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
However, when the chain is split after more steps, samples share high-level attributes like gender,
hair color, eyewear, saturation, pose and facial expression. This indicates that intermediate latents
like x750 encode these attributes, despite their imperceptibility. | Denoising Diffusion Probabilistic Models |
specified time. Due to the open-world nature of Minecraft,
the world and initial position that the agent is spawned at
could vary a lot. Therefore, we conducted at least 30 tests
for each task using different seeds and reported the average | JARVIS-1 |
Scaling of specialized models. We observe that scaling the number of parameters matters for models
specialized for coding. With the same training process, our larger models outperform their smaller counterparts
on almost every metric from HumanEval, MBPP and APPS (Table 2, 3). For instance, we gain 5.6 percentage
point... | CodeLlama2 |
a lot of data and no distribution shift – two examples being language modeling/span corruption and
machine translation (Shazeer et al., 2017; Lepikhin et al., 2020; Kim et al., 2021; Fedus et al., 2021).
In contrast, discrepancies between strong pre-training quality and poor fine-tuning quality for sparse
models have be... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Total
Size
400
800
800
400
4,000
1,800
1,200
9,400
Table 11: Templates used to create DATESET where
a current_date is randomly selected. For each cur-
rent_date, a random past_date and future_date is gen-
erated and used to fill each template, if relevant. The
federal holidays in the United States (e.g., Thank... | Toolformer |
As this brief analysis highlights, the end result of the Roommates.com
holding is ambiguous and depends a great deal on the platform in
in that case.41
question, a critique that was voiced by the dissent
However,
it seems clear that under certain circumstances platforms
might not enjoy the benefits of CDA 230 immunity, ... | Social_Media_and_Democracy |
This approach would create exceptions to CDA 230 for a number of existing
laws and potential new regulations. Portions of the Federal Election Campaign
Act (FECA) would be excepted from CDA 230 to block foreign interference into
political discourse.66 FECA prohibits foreign interests from engaging in
election-related s... | Social_Media_and_Democracy |
=1 = {(v = 1)} and vars(s)
:=1 = {(u = 1), (v = 1)}.
Unless otherwise specified, states will be assumed total and we will usually write state rather than total state. As a
convention, we will often tacitly assume that binary variables have the domain {0, 1} and use the shorthands v f... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
14
⇥⇥X1X1⇥⇥X2X2X3X3(b)Variableorder:X1àX2àX3X1X1X1X1X2X2X3X3X1X1X1X1X2X2X3X3X1X1X1X1X2X2X3X3X1X1X2X2X3X3X3X3X1X1X2X2X3X3X3X3X1X1X2X2X3X3X3X3(c)Variableorder:X3àX2àX1(a)ààNeedtoevaluate2+9+9=20PCunitsintotal.ààNeedtoevaluate2+6+5=13PCunitsintotal.p(X3=x3,X2=x2,X1=x1)p(X3=x3,X2=x2)p(X3=x3)p(X1=x1,X2=x2,X3=x3)p(X1=x1,X2=... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
recognition. The Conformer model consists of several building blocks, including convolutional
layers, self-attention layers, and feedforward layers. The architecture of the Conformer model can
be summarized as follows:
Mel spectrograms.
input sequence through convolutional layers. | AReviewofDeepLearningTechniquesforSpeechProcessing |
transformers facilitate modeling long-range dependencies within the signal. These developments
have led to substantial improvements in the performance and versatility of speech processing
systems, unlocking new possibilities for applications in diverse domains. | AReviewofDeepLearningTechniquesforSpeechProcessing |
If applying to an external funding agency, remember that the reviewer may not be an expert in your field of research
Stick to the guidelines and remember the deadline
Student Recruitment & Admissionswww.ed.ac.uk/student-recruitment 5
Content and style of your research proposal
What to put in your proposal?
Applic... | research proposal guidance |
https://a16z.com/the-future-of-music-how-generative-ai-is-transforming-the-music-industry/
7/12
14/11/2023, 13:39
The Future of Music: How Generative AI Is Transforming the Music Industry | Andreessen Horowitz
Stem separation: Decompiling a song into stems including vocals, bassline, and
percussion using technolog... | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
Figure 14: Effectiveness of the proposed style injection module. Top: without Gaussian noise; middle: without token
embeddings; bottom: full model. The prompt is “a wolf sitting on books and wearing a tie and wearing a party hat”. The
comparison shows that the incorporation of Gaussian noise prevents training collapse,... | Instant3D |
((cid:52)) The bottom line, experts said, is that there is an extremely low risk of con-
tracting coronavirus from the food supply.
((cid:55)) More than 8,500 people in the United States have been infected with coron-
avirus, and that number changes significantly by the hour.
((cid:55)) There are more than 18,000 cases ... | Language models trained on media diets can predict public opinion |
Hugo Touvron, Louis Martin, Kevin Stone, Peter Al-
bert, Amjad Almahairi, Yasmine Babaei, Nikolay
Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti
Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton
Ferrer, Moya Chen, Guillem Cucurull, David Esiobu,
Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Veda... | AppAgents |
p(y | z, x) =
p(yj | z, x) ∝ exp(cid:0)w(cid:62)
j=1
j BERTMASK(j)(joinBERT(x, zbody))(cid:1)
where BERTMASK(j) denotes the Transformer output vector
corresponding to the jth masked token, Jx is the total num-
ber of [MASK] tokens in x, and wj is a learned word em-
bedding for token yj.
For Open-QA fine-tuning, we w... | REALM |
Costa Crociere is the owner of the AIDAstella and are based in Genoa. The operator of AIDAstella is AIDA
Cruises.
(Euro, currency, Netherlands), (Stellendam, birthPlace, Ab Klink ), (Netherlands, nationality, Ab Klink)
Ab Klink was born in Stellendam and is a national of the Netherlands where the currency is the Euro.
... | Prefix-Tuning |
In Figure 33, we compare the test PM score for all policy sizes and all test PM sizes. The main observation
here is that the slope of the graph increases with respect to test PM size, thus suggesting that larger test
PM’s are significantly more capable of distinguishing policy performance. In other words, larger prefere... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
In their survey article, Burkart and Huber [13] provided a detailed introduction to explainable supervised machine
learning models that focused on defining and classifying the various approaches in the field. In doing so, they discussed
the reasons for explainability in machine learning models and highlighted the role ... | Knowledge-graph-based explainable AI- A systematic review |
8
Model
CodeGen-Multi
CodeGeeX
code-cushman-001
StarCoder Base
StarCoder Python
Llama-v2
Code Llama
Code Llama - Instruct
Code Llama - Python
Size
Multi-lingual Human-Eval
TS
C#
PHP
C++ Java | CodeLlama2 |
sample 5 second clips around each time-stamp. The dataset
is split randomly such that we have 510,142 clips for train- | IMAGEBIND- One Embedding Space To Bind Them A |
[112] Malladi, S., Gao, T., Nichani, E., Damian, A., Lee, J.D., Chen, D., Arora, S.:
Fine-Tuning Language Models with Just Forward Passes (2023)
[113] Wu, C., Lin, W., Zhang, X., Zhang, Y., Wang, Y., Xie, W.: PMC-LLaMA:
Towards Building Open-source Language Models for Medicine (2023)
[114] Kumar, A., Raghunathan, A... | Beyond Efficiency |
demonstrate that they produce believable simulacra of both in-
dividual and emergent group behavior. Generative agents draw
a wide variety of inferences about themselves, other agents, and
their environment; they create daily plans that reflect their char-
acteristics and experiences, act out those plans, react, and re... | Generative Agents- Interactive Simulacra of Human Behavior |
within the same output sequence.
8.2 Ecosystem of RAG
Downstream Tasks and Evaluation
By integrating relevant information from a broad knowledge
base, RAG has demonstrated significant potential in enhanc-
ing language models’ ability to process complex queries and
generate information-rich responses. Numerous studies h... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Specifically, Oogiri-GO is a multimodal and multilingual
humor dataset, and contains more than 130,000 Oogiri sam-
ples in English, Chinese, and Japanese. Notably, in Oogiri-
GO, 77.95% of samples are annotated with human prefer-
ences, namely the number of likes, indicating the popularity
of a response. As illustrated... | Let’sThinkOutsidetheBox |
edly, many of the most cited research papers dealing with
LLMs, including many papers that introduce new methods
or theories, are not published in peer-reviewed venues. The
recent trend toward limiting access to LLMs and treating
the details of LLM training as proprietary information is
also an obstacle to scientific st... | Eight Things to Know about Large Language Models |
While OBM principals might be used to automatically change a programme’s duration to meet our time
requirements [2] or make a story more relatable to us by including local information based on our
location [3], there are also substantial implications for accessibility. As the content creat... | informatics-phd-projects-2022-23 |
ViT-L/14
CLIP
ViT-L/14336
CLIP
SWAG
ViT-H/14
OpenCLIP ViT-H/14
OpenCLIP ViT-G/14
EVA-CLIP ViT-g/14
MAE
DINO
SEERv2
MSN
EsViT
Mugs
iBOT
DINOv2
ViT-H/14
ViT-S/8
RG10B
ViT-L/7
Swin-B/W=14
ViT-L/16
ViT-L/16
ViT-S/14
ViT-B/14
ViT-L/14
ViT-g/14
WIT-400M
WIT-400M
IG3.6B
LAION
LAION
custom∗
INet-1k
INet-1k
IG2B
INet-1k
IN... | DINOv2- Learning Robust Visual Features without Supervision |
value for this outcome, which can obviously be different than her true value. As we show in
Theorem 1, first price contracts can be very socially inefficient (where social efficiency is considered
in equilibrium, through the standard measures of price of anarchy/stability).
A solution designed for complete information. A ... | Incomplete Information VCG Contracts for Common Agency |
The vast amounts of human-generated data LLMs are trained on enables them to
mimic human characteristics in their outputs and enact convincing personas—in other
words, exhibit a form of synthetic personality. Personality is the characteristic set of
an individual’s patterns of thought, set of traits, and behaviors [17,... | PersonalityTraitsinLargeLanguageModels |
References
[1] Model index for researchers, 2023. 2
[2] Thomas Anthony, Zheng Tian, and David Barber. Thinking
fast and slow with deep learning and tree search. Neural
Information Processing Systems, 2017. 2
[3] Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain,
Deep Ganguli, Tom Henighan, Andy Jones, Nicholas
Joseph, ... | DiffusionModelAlignmentUsing Direct Preference Optimization |
coordinate embedding further improves rendering quality.
Combining static and dynamic models improves quality for
static elements, as seen in metrics over full scenes. Finally,
removing supervision from motion segmentation or regu-
larization reduces overall rendering quality, demonstrating
the value of proposed losses... | DynIBaR-NeuralDynamicImage-BasedRendering |
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