Link/DOI string | Publication Date timestamp[ns] | Title string | Authors string | Abstract string | Categories string | label int64 | source string | Classification_embedding list | Proximity_embedding list | top_10_similar string | max_similarity float64 | avg_similarity float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
http://arxiv.org/abs/2412.21088v1 | 2024-12-30T00:00:00 | Advances in Multi-agent Reinforcement Learning: Persistent Autonomy and Robot Learning Lab Report 2024 | Reza Azadeh | Multi-Agent Reinforcement Learning (MARL) approaches have emerged as popular solutions to address the general challenges of cooperation in multi-agent environments, where the success of achieving shared or individual goals critically depends on the coordination and collaboration between agents. However, existing cooper... | cs.MA | 0 | Random | [
-1.1581405401229858,
-0.7987884283065796,
0.3688540756702423,
-0.6881622672080994,
-0.26119449734687805,
0.3850988447666168,
0.417211651802063,
0.5866531133651733,
-0.021215246990323067,
0.7102983593940735,
-1.358616828918457,
1.0955184698104858,
0.07884883880615234,
0.21428857743740082,
... | [
-0.21384118497371674,
0.5765016674995422,
-0.42355337738990784,
0.407161146402359,
-0.06808453798294067,
0.24806535243988037,
0.42637547850608826,
-0.014840764924883842,
-0.16723990440368652,
0.8677188158035278,
-0.15535874664783478,
0.42822396755218506,
-0.06093866378068924,
0.23098455369... | {"http://arxiv.org/abs/2412.20523v1": 0.9643830060958862, "http://arxiv.org/abs/2411.08400v1": 0.956005871295929, "http://arxiv.org/abs/2412.15700v2": 0.9520399570465088, "http://arxiv.org/abs/2401.00132v3": 0.9514643549919128, "http://arxiv.org/abs/2411.19639v1": 0.9507791996002197, "http://arxiv.org/abs/2412.15639v2"... | 0.964383 | 0.9523 |
http://arxiv.org/abs/2412.21103v1 | 2024-12-30T00:00:00 | Parallel DNA Sequence Alignment on High-Performance Systems with CUDA and MPI | Linus Zwaka | Sequence alignment is a cornerstone of bioinformatics, widely used to identify similarities between DNA, RNA, and protein sequences and studying evolutionary relationships and functional properties. The Needleman-Wunsch algorithm remains a robust and accurate method for global sequence alignment. However, its computati... | cs.DC | 0 | Random | [
1.1332168579101562,
-0.5949282646179199,
-1.342690110206604,
-0.5527603030204773,
-0.37915727496147156,
-1.8804298639297485,
-1.2396140098571777,
1.4175736904144287,
-0.9174191951751709,
0.45878565311431885,
0.6513866782188416,
-0.8845067620277405,
0.6713045239448547,
0.0635405033826828,
... | [
0.8031516075134277,
-0.25099509954452515,
-0.43405425548553467,
0.2485923171043396,
-0.5920303463935852,
0.1434619277715683,
0.0014479756355285645,
-0.055575426667928696,
-0.4049106538295746,
0.27905407547950745,
0.3324086666107178,
-0.48451972007751465,
0.2784245014190674,
-0.250421166419... | {"http://arxiv.org/abs/2409.06075v1": 0.954102635383606, "http://arxiv.org/abs/2412.08308v3": 0.9359860420227051, "http://arxiv.org/abs/2411.03832v2": 0.9332360029220581, "http://arxiv.org/abs/2406.10362v1": 0.9310359954833984, "http://arxiv.org/abs/2407.07718v1": 0.9288723468780518, "http://arxiv.org/abs/2411.11547v1"... | 0.954103 | 0.929944 |
http://arxiv.org/abs/2412.21188v1 | 2024-12-30T00:00:00 | Sparse chaos in cortical circuits | Rainer Engelken; Michael Monteforte; Fred Wolf | Nerve impulses, the currency of information flow in the brain, are generated by an instability of the neuronal membrane potential dynamics. Neuronal circuits exhibit collective chaos that appears essential for learning, memory, sensory processing, and motor control. However, the factors controlling the nature and inten... | q-bio.NC; cond-mat.dis-nn; cs.LG; nlin.CD | 0 | Random | [
0.19945186376571655,
-0.4436161518096924,
-0.9062685966491699,
-0.11719714850187302,
0.35868120193481445,
0.251276433467865,
-0.5598607659339905,
0.4717603325843811,
-0.6570152640342712,
0.7880330681800842,
-0.7498202323913574,
0.42096656560897827,
-0.43073171377182007,
0.233225017786026,
... | [
0.2087130844593048,
0.4854946434497833,
-0.6386000514030457,
0.59512859582901,
-0.293803870677948,
-0.3866843581199646,
0.2107487916946411,
-0.40112847089767456,
-0.11951437592506409,
0.1163376122713089,
0.43549445271492004,
0.13720236718654633,
0.0719749927520752,
0.4286035895347595,
-0... | {"http://arxiv.org/abs/2410.06361v2": 0.9348133206367493, "http://arxiv.org/abs/2410.19348v1": 0.9279143214225769, "http://arxiv.org/abs/2411.06802v3": 0.9191324710845947, "http://arxiv.org/abs/2408.00109v3": 0.9183610081672668, "http://arxiv.org/abs/2412.05126v1": 0.9180212020874023, "http://arxiv.org/abs/2411.17692v1... | 0.934813 | 0.919809 |
http://arxiv.org/abs/2412.20885v1 | 2024-12-30T00:00:00 | CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks | Chenjie Xie; Li You; Zhenzhou Jin; Jinke Tang; Xiqi Gao; Xiang-Gen Xia | Multi-band massive multiple-input multiple-output (MIMO) communication can promote the cooperation of licensed and unlicensed spectra, effectively enhancing spectrum efficiency for Wi-Fi and other wireless systems. As an enabler for multi-band transmission, channel fingerprints (CF), also known as the channel knowledge... | cs.IT; cs.LG; eess.SP; math.IT | 0 | Random | [
0.07198256999254227,
0.10179996490478516,
-0.7746948003768921,
-0.15763777494430542,
-0.42549628019332886,
0.5462244749069214,
-0.4137094020843506,
-0.09760478883981705,
0.8262760639190674,
0.04553545266389847,
0.02749549224972725,
-0.3274080753326416,
0.18441039323806763,
0.02560208365321... | [
0.9425034523010254,
0.618135392665863,
-0.5308977961540222,
0.4491267800331116,
-0.3272516131401062,
0.190777987241745,
0.1447068154811859,
-1.0090899467468262,
0.6493102312088013,
-0.1417478621006012,
0.5387150049209595,
-0.06741154938936234,
-0.24857103824615479,
-0.2358672022819519,
-... | {"http://arxiv.org/abs/2409.13494v2": 0.9518524408340454, "http://arxiv.org/abs/2412.18281v1": 0.9447777271270752, "http://arxiv.org/abs/2411.16971v1": 0.944582462310791, "http://arxiv.org/abs/2409.02564v2": 0.9411053657531738, "http://arxiv.org/abs/2409.16420v1": 0.9385129809379578, "http://arxiv.org/abs/2412.09839v3"... | 0.951852 | 0.939067 |
http://arxiv.org/abs/2412.20916v1 | 2024-12-30T00:00:00 | Low-Light Image Enhancement via Generative Perceptual Priors | Han Zhou; Wei Dong; Xiaohong Liu; Yulun Zhang; Guangtao Zhai; Jun Chen | Although significant progress has been made in enhancing visibility, retrieving texture details, and mitigating noise in Low-Light (LL) images, the challenge persists in applying current Low-Light Image Enhancement (LLIE) methods to real-world scenarios, primarily due to the diverse illumination conditions encountered.... | cs.CV | 0 | Random | [
-0.08155880868434906,
-0.7345039248466492,
-0.12910448014736176,
-0.8253903985023499,
-0.4278126358985901,
0.5603204965591431,
0.9470382332801819,
-0.13092342019081116,
0.02616824395954609,
-0.9778296947479248,
-0.0009984970092773438,
1.406111717224121,
0.9519143104553223,
-0.0014599338173... | [
0.9323173761367798,
0.3395253121852875,
0.1856069564819336,
0.17945921421051025,
-0.436936616897583,
0.25389954447746277,
0.4495360255241394,
-0.32762283086776733,
-0.009958034381270409,
-0.5688987374305725,
0.48988232016563416,
0.6177219152450562,
-0.19921264052391052,
-0.4754803776741028... | {"http://arxiv.org/abs/2312.16805v3": 0.9589101076126099, "http://arxiv.org/abs/2408.09650v1": 0.9412907361984253, "http://arxiv.org/abs/2409.07451v1": 0.9356213808059692, "http://arxiv.org/abs/2412.00177v3": 0.9320597052574158, "http://arxiv.org/abs/2411.05005v1": 0.9319079518318176, "http://arxiv.org/abs/2410.13566v1... | 0.95891 | 0.935519 |
http://arxiv.org/abs/2501.01451v1 | 2024-12-30T00:00:00 | Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research | Maryna Kapitonova; Tonio Ball | Recently, there is an increasing interest in using artificial intelligence (AI) to automate aspects of the research process, or even autonomously conduct the full research cycle from idea generation, over data analysis, to composing and evaluation of scientific manuscripts. Examples of working AI scientist systems have... | cs.HC; cs.AI | 0 | Random | [
0.6271460652351379,
1.3173977136611938,
0.8750019073486328,
-0.8525891304016113,
0.5250859260559082,
0.4929039478302002,
0.7788019180297852,
-0.226295605301857,
0.08419694751501083,
-0.32951903343200684,
-0.42525815963745117,
-0.6175549030303955,
0.8546807765960693,
0.2145031839609146,
-... | [
0.15092389285564423,
0.6333712339401245,
-0.027844194322824478,
0.12408259510993958,
-0.5453829765319824,
0.358537882566452,
0.5701322555541992,
-0.5055094957351685,
-0.22252923250198364,
0.21902203559875488,
0.5778281688690186,
-0.1650892049074173,
0.25546208024024963,
0.6480862498283386,... | {"http://arxiv.org/abs/2411.16723v1": 0.922608494758606, "http://arxiv.org/abs/2412.20297v1": 0.9197981357574463, "http://arxiv.org/abs/2410.11864v1": 0.919283390045166, "http://arxiv.org/abs/2407.07061v2": 0.9187513589859009, "http://arxiv.org/abs/2406.07155v3": 0.9179937839508057, "http://arxiv.org/abs/2410.11590v1":... | 0.922608 | 0.917789 |
http://arxiv.org/abs/2412.20799v1 | 2024-12-30T00:00:00 | SFE-Net: Harnessing Biological Principles of Differential Gene Expression for Improved Feature Selection in Deep Learning Networks | Yuqi Li; Yuanzhong Zheng; Yaoxuan Wang; Jianjun Yin; Haojun Fei | In the realm of DeepFake detection, the challenge of adapting to various synthesis methodologies such as Faceswap, Deepfakes, Face2Face, and NeuralTextures significantly impacts the performance of traditional machine learning models. These models often suffer from static feature representation, which struggles to perfo... | cs.MM | 0 | Random | [
-1.0230493545532227,
-0.6190851926803589,
-0.15210680663585663,
-0.9899656772613525,
-0.1774779111146927,
-0.6470932960510254,
0.38842764496803284,
0.029290825128555298,
-0.2663308382034302,
-0.64691162109375,
-0.11572559177875519,
2.187145233154297,
0.8216262459754944,
0.2205783873796463,... | [
0.1870799958705902,
0.07900804281234741,
0.1945507973432541,
-0.16848844289779663,
-0.001276317983865738,
-0.14507736265659332,
0.5714221000671387,
-0.8342801332473755,
0.11270377784967422,
-0.1581154465675354,
0.2821621000766754,
0.31555962562561035,
0.12349899113178253,
-0.11454270780086... | {"http://arxiv.org/abs/2408.01668v1": 0.9409390687942505, "http://arxiv.org/abs/2411.19715v3": 0.9311567544937134, "http://arxiv.org/abs/2312.08020v1": 0.9280364513397217, "http://arxiv.org/abs/2410.07888v1": 0.9267876744270325, "http://arxiv.org/abs/2411.05335v3": 0.9251755475997925, "http://arxiv.org/abs/2406.13295v1... | 0.940939 | 0.926486 |
http://arxiv.org/abs/2412.20753v1 | 2024-12-30T00:00:00 | Simple Quantum Coins Enable Pretty Good State Transfer on Every Hypercube | Hanmeng Zhan | We consider pretty good state transfer in coined quantum walks between antipodal vertices on the hypercube $Q_d$. When $d$ is a prime, this was proven to occur in the arc-reversal walk with Grover coins. We extend this result by constructing weighted Grover coins that enable pretty good state transfer on every $Q_d$. O... | math.CO; cs.DM; quant-ph | 0 | Random | [
-0.08287569880485535,
0.5293521881103516,
-0.2797922194004059,
0.7728647589683533,
-0.47466954588890076,
-1.198999047279358,
-0.4828827977180481,
0.4053919017314911,
0.07281145453453064,
0.05835060402750969,
-0.7915903329849243,
0.32221561670303345,
-0.5031758546829224,
-1.4321203231811523... | [
0.55231773853302,
0.7294436693191528,
-0.426565945148468,
0.4950207769870758,
-0.5144219994544983,
-0.47031110525131226,
0.3370952010154724,
-0.3943045735359192,
-0.5015223622322083,
0.1495087444782257,
-0.1889992654323578,
0.10548748075962067,
0.015662025660276413,
-0.5986684560775757,
... | {"http://arxiv.org/abs/2411.18581v2": 0.9499003887176514, "http://arxiv.org/abs/2409.14694v1": 0.9450747966766357, "http://arxiv.org/abs/2410.04409v2": 0.9437260627746582, "http://arxiv.org/abs/2407.14398v2": 0.9436769485473633, "http://arxiv.org/abs/2411.16676v2": 0.9436591863632202, "http://arxiv.org/abs/2407.02530v1... | 0.9499 | 0.940425 |
http://arxiv.org/abs/2412.21127v2 | 2024-12-30T00:00:00 | What Makes for a Good Stereoscopic Image? | Netanel Y. Tamir; Shir Amir; Ranel Itzhaky; Noam Atia; Shobhita Sundaram; Stephanie Fu; Ron Sokolovsky; Phillip Isola; Tali Dekel; Richard Zhang; Miriam Farber | With rapid advancements in virtual reality (VR) headsets, effectively measuring stereoscopic quality of experience (SQoE) has become essential for delivering immersive and comfortable 3D experiences. However, most existing stereo metrics focus on isolated aspects of the viewing experience such as visual discomfort or i... | cs.CV | 0 | Random | [
-1.058607816696167,
-0.5886498689651489,
-0.06216978654265404,
-1.1549934148788452,
-0.2234654575586319,
0.24711668491363525,
1.0733445882797241,
-0.37626913189888,
0.826450765132904,
-1.0939124822616577,
0.04925667867064476,
1.448991298675537,
1.113958716392517,
-0.1576446294784546,
0.1... | [
0.17434191703796387,
0.06403322517871857,
-0.19582995772361755,
-0.360665887594223,
-0.1899874061346054,
0.029971402138471603,
-0.23451849818229675,
-0.42026907205581665,
-0.046162527054548264,
-0.5023159384727478,
0.8986778259277344,
0.737462043762207,
-0.21631434559822083,
0.164584919810... | {"http://arxiv.org/abs/2407.21363v2": 0.9475986361503601, "http://arxiv.org/abs/2412.20423v2": 0.9443246126174927, "http://arxiv.org/abs/2408.10134v1": 0.9335275888442993, "http://arxiv.org/abs/2406.09313v2": 0.9290494918823242, "http://arxiv.org/abs/2411.19522v1": 0.9210882782936096, "http://arxiv.org/abs/2405.20078v3... | 0.947599 | 0.927358 |
http://arxiv.org/abs/2412.21181v1 | 2024-12-30T00:00:00 | Causal Hangover Effects | Andreas Santucci; Eric Lax | It's not unreasonable to think that in-game sporting performance can be affected partly by what takes place off the court. We can't observe what happens between games directly. Instead, we proxy for the possibility of athletes partying by looking at play following games in party cities. We are interested to see if team... | econ.EM; cs.IT; math.IT; stat.AP | 0 | Random | [
-1.303796410560608,
-0.5991570353507996,
-1.3708730936050415,
-0.31643375754356384,
0.09609799087047577,
-0.18869717419147491,
0.48571985960006714,
0.08688978105783463,
-1.5299237966537476,
0.6817219257354736,
-0.8204753994941711,
0.9112660884857178,
0.34644293785095215,
-0.721131265163421... | [
-0.4518811106681824,
0.2522314786911011,
-0.6290691494941711,
0.19171437621116638,
-1.1419694423675537,
-0.1516469269990921,
0.3177304267883301,
-0.46234074234962463,
-0.23929746448993683,
0.46579688787460327,
0.4358157813549042,
0.21880804002285004,
-0.6253116726875305,
0.0294004529714584... | {"http://arxiv.org/abs/2301.04001v1": 0.905997097492218, "http://arxiv.org/abs/2306.01740v4": 0.9008141756057739, "http://arxiv.org/abs/2410.00978v2": 0.8985057473182678, "http://arxiv.org/abs/2410.21484v1": 0.8933658003807068, "http://arxiv.org/abs/2411.01057v1": 0.8914952278137207, "http://arxiv.org/abs/2402.10243v1"... | 0.905997 | 0.892833 |
http://arxiv.org/abs/2412.20634v3 | 2024-12-30T00:00:00 | Graph Neural Networks for Next-Generation-IoT: Recent Advances and Open Challenges | Nguyen Xuan Tung; Le Tung Giang; Bui Duc Son; Seon Geun Jeong; Trinh Van Chien; Won Joo Hwang; Lajos Hanzo | Graph Neural Networks (GNNs) have emerged as a powerful framework for modeling complex interconnected systems, hence making them particularly well-suited to address the growing challenges of next-generation Internet of Things (NG-IoT) networks. Existing studies remain fragmented, and there is a lack of comprehensive gu... | cs.IT; math.IT | 0 | Random | [
0.40635621547698975,
0.14666663110256195,
-0.9335545301437378,
-0.4863361716270447,
-0.3070741295814514,
0.04819733276963234,
-0.31600484251976013,
0.24616801738739014,
0.9168586730957031,
0.07165775448083878,
-0.2924111485481262,
0.3396396040916443,
0.32851096987724304,
0.3091082572937011... | [
0.8164960741996765,
1.238781213760376,
-0.8095242381095886,
0.6221356391906738,
0.22843827307224274,
0.12820690870285034,
0.33814939856529236,
-0.3594144284725189,
0.2024606317281723,
-0.08885809779167175,
0.2552938461303711,
-0.025036631152033806,
-0.016034133732318878,
-0.112661093473434... | {"http://arxiv.org/abs/2411.01312v2": 0.9355654716491699, "http://arxiv.org/abs/2412.12739v1": 0.9332586526870728, "http://arxiv.org/abs/2411.01924v1": 0.9309108257293701, "http://arxiv.org/abs/2412.16779v1": 0.9297161102294922, "http://arxiv.org/abs/2412.06105v1": 0.9291796684265137, "http://arxiv.org/abs/2409.15695v1... | 0.935565 | 0.929605 |
http://arxiv.org/abs/2412.20881v1 | 2024-12-30T00:00:00 | LiDAR-Camera Fusion for Video Panoptic Segmentation without Video Training | Fardin Ayar; Ehsan Javanmardi; Manabu Tsukada; Mahdi Javanmardi; Mohammad Rahmati | Panoptic segmentation, which combines instance and semantic segmentation, has gained a lot of attention in autonomous vehicles, due to its comprehensive representation of the scene. This task can be applied for cameras and LiDAR sensors, but there has been a limited focus on combining both sensors to enhance image pano... | cs.CV | 0 | Random | [
-0.631888747215271,
-0.8591349720954895,
-0.325362890958786,
-1.3946583271026611,
-0.3141443133354187,
0.5564579963684082,
0.0956108421087265,
-0.12231207638978958,
-0.1793225109577179,
-0.11832569539546967,
-0.3879234790802002,
1.4442110061645508,
0.9448592662811279,
-1.6537564992904663,
... | [
0.2256631702184677,
0.09558333456516266,
-0.44886136054992676,
-0.243402361869812,
-0.5348950028419495,
0.1639726161956787,
-0.2545906901359558,
-0.34082597494125366,
-0.3561590015888214,
-0.9907499551773071,
0.12606407701969147,
0.8158934116363525,
0.3487209379673004,
-0.5001219511032104,... | {"http://arxiv.org/abs/2412.12740v1": 0.9460364580154419, "http://arxiv.org/abs/2411.15016v1": 0.9421290159225464, "http://arxiv.org/abs/2411.05311v1": 0.9341452717781067, "http://arxiv.org/abs/2411.00600v1": 0.9338035583496094, "http://arxiv.org/abs/2405.20986v2": 0.9317331314086914, "http://arxiv.org/abs/2411.06632v1... | 0.946036 | 0.93099 |
http://arxiv.org/abs/2412.20810v1 | 2024-12-30T00:00:00 | TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting | Huanyu Zhang; Chang Xu; Yi-Fan Zhang; Zhang Zhang; Liang Wang; Jiang Bian; Tieniu Tan | Time series forecasting plays a crucial role in data mining, driving rapid advancements across numerous industries. With the emergence of large models, time series foundation models (TSFMs) have exhibited remarkable generalization capabilities, such as zero-shot learning, through large-scale pre-training. Meanwhile, Re... | cs.LG | 0 | Random | [
-1.8403396606445312,
-1.3886500597000122,
-0.2810456454753876,
-0.8950153589248657,
-0.5422720313072205,
0.2125094085931778,
0.3105303645133972,
-0.010765006765723228,
-0.5784156918525696,
-0.03919374197721481,
-0.6379510164260864,
1.5671433210372925,
0.28021880984306335,
0.127861514687538... | [
0.02404084801673889,
0.45191729068756104,
-0.5187150835990906,
-0.15187829732894897,
-0.23386001586914062,
0.06007689610123634,
0.4888850152492523,
-0.3109053373336792,
-0.0321534089744091,
0.2254907488822937,
0.4199661612510681,
0.06817582994699478,
0.062324050813913345,
-0.17557317018508... | {"http://arxiv.org/abs/2310.01728v2": 0.9556836485862732, "http://arxiv.org/abs/2412.15315v1": 0.9441444873809814, "http://arxiv.org/abs/2412.17285v1": 0.9438999891281128, "http://arxiv.org/abs/2305.12095v5": 0.9428966045379639, "http://arxiv.org/abs/2412.19286v1": 0.9408551454544067, "http://arxiv.org/abs/2410.09487v2... | 0.955684 | 0.940433 |
http://arxiv.org/abs/2412.21016v2 | 2024-12-30T00:00:00 | Assessing the Robustness of LLM-based NLP Software via Automated Testing | Mingxuan Xiao; Yan Xiao; Shunhui Ji; Hanbo Cai; Lei Xue; Pengcheng Zhang | Benefiting from the advancements in LLMs, NLP software has undergone rapid development. Such software is widely employed in various safety-critical tasks, such as financial sentiment analysis, toxic content moderation, and log generation. Unlike traditional software, LLM-based NLP software relies on prompts and example... | cs.SE | 0 | Random | [
-1.1114189624786377,
-0.9034470915794373,
-0.2705181837081909,
-1.341233253479004,
-0.10253036022186279,
-1.15218985080719,
-0.12414194643497467,
0.5810188055038452,
-0.2625121474266052,
-0.3689226508140564,
-1.0552457571029663,
0.5953118205070496,
0.40625572204589844,
-0.08179564774036407... | [
0.03860604390501976,
0.22119978070259094,
-0.32902243733406067,
-0.09343426674604416,
-0.3404526114463806,
-1.2185050249099731,
0.2747882604598999,
0.03601065278053284,
-0.33158719539642334,
0.03750983253121376,
-0.09452164173126221,
-0.637201189994812,
0.17180520296096802,
-0.215933799743... | {"http://arxiv.org/abs/2401.00287v1": 0.9412485361099243, "http://arxiv.org/abs/2408.10474v1": 0.9397873878479004, "http://arxiv.org/abs/2310.09652v1": 0.9366211891174316, "http://arxiv.org/abs/2408.15207v3": 0.9355692267417908, "http://arxiv.org/abs/2412.10617v1": 0.9317336678504944, "http://arxiv.org/abs/2412.10807v2... | 0.941249 | 0.933111 |
http://arxiv.org/abs/2412.20834v1 | 2024-12-30T00:00:00 | Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment | Jianfei Zhang; Jun Bai; Bei Li; Yanmeng Wang; Rumei Li; Chenghua Lin; Wenge Rong | Aligning Large Language Models (LLMs) with general human preferences has been proved crucial in improving the interaction quality between LLMs and human. However, human values are inherently diverse among different individuals, making it insufficient to align LLMs solely with general preferences. To address this, perso... | cs.CL; cs.AI | 0 | Random | [
-0.4320405125617981,
-0.08549080789089203,
0.15012440085411072,
-0.9654066562652588,
0.1848684847354889,
-0.556291401386261,
1.02982497215271,
0.4842224419116974,
-1.2676959037780762,
-1.0201616287231445,
-0.2195734828710556,
0.08103496581315994,
0.760811448097229,
1.1042981147766113,
0.... | [
0.09014621376991272,
0.4427392780780792,
-0.6259152293205261,
-0.38450682163238525,
-0.5336503982543945,
-0.28729283809661865,
0.30488550662994385,
-0.15860334038734436,
-0.4714413583278656,
0.11294177919626236,
0.540884256362915,
0.3142523765563965,
-0.33720505237579346,
0.008862450718879... | {"http://arxiv.org/abs/2310.13011v2": 0.9668805599212646, "http://arxiv.org/abs/2406.04412v2": 0.9606906175613403, "http://arxiv.org/abs/2406.09760v2": 0.9562739133834839, "http://arxiv.org/abs/2407.07880v2": 0.955022394657135, "http://arxiv.org/abs/2408.07471v4": 0.9549579620361328, "http://arxiv.org/abs/2312.15997v3"... | 0.966881 | 0.956108 |
http://arxiv.org/abs/2501.00174v2 | 2024-12-30T00:00:00 | The Text Classification Pipeline: Starting Shallow going Deeper | Marco Siino; Ilenia Tinnirello; Marco La Cascia | Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep learning revolutionize text classification, propelling advancements in text retrieval, categorization, information extractio... | cs.CL; cs.AI; cs.IR | 0 | Random | [
-1.0502903461456299,
-0.15978892147541046,
0.21620088815689087,
-0.5500317215919495,
-0.2324763536453247,
-0.756203830242157,
1.3887083530426025,
0.7896298766136169,
-1.4821674823760986,
-0.13860416412353516,
-0.06533288955688477,
1.1520020961761475,
0.4926862418651581,
1.0216777324676514,... | [
0.33944737911224365,
0.34725111722946167,
-0.5327163338661194,
-0.23656290769577026,
-0.538520097732544,
-0.3679412603378296,
0.810512900352478,
0.1431972086429596,
-0.3817506432533264,
-0.22980289161205292,
0.7086367607116699,
-0.3711811304092407,
0.07515721023082733,
-0.1664663851261139,... | {"http://arxiv.org/abs/2309.10654v2": 0.9330360889434814, "http://arxiv.org/abs/2404.18942v2": 0.9306243062019348, "http://arxiv.org/abs/2412.12754v1": 0.9287168979644775, "http://arxiv.org/abs/2404.08403v1": 0.9260851740837097, "http://arxiv.org/abs/2312.16104v1": 0.9256046414375305, "http://arxiv.org/abs/2308.16361v2... | 0.933036 | 0.925221 |
http://arxiv.org/abs/2412.20755v2 | 2024-12-30T00:00:00 | Ultra-Wideband Double-Directional Channel Measurements and Statistical Modeling in Urban Microcellular Environments for the Upper-Midband/FR3 | Naveed A. Abbasi; Kelvin Arana; Siddhant Singh; Atulya Bist; Vikram Vasudevan; Tathagat Pal; Jorge Gomez-Ponce; Young-Han Nam; Charlie Zhang; Andreas F. Molisch | The upper midband, designated as Frequency Range 3 (FR3), is increasingly critical for the next-generation of wireless networks. Channel propagation measurements and their statistical analysis are essential first steps towards this direction. This paper presents a comprehensive ultra-wideband (UWB) double-directional c... | eess.SY; cs.SY | 0 | Random | [
0.16845276951789856,
-0.10565401613712311,
-0.6434345245361328,
-0.06489432603120804,
-0.6264709830284119,
-0.07461407780647278,
-0.8032317161560059,
0.2975708246231079,
0.4175579845905304,
0.23540626466274261,
0.13219882547855377,
-0.33889520168304443,
0.28169021010398865,
-0.080390758812... | [
0.5575777292251587,
0.609719455242157,
-0.14016592502593994,
0.4737723767757416,
-0.8043237924575806,
0.1667710542678833,
-0.5058385729789734,
-0.06509149819612503,
-0.06823138892650604,
0.31357336044311523,
0.33156922459602356,
-0.6637770533561707,
-0.060366030782461166,
0.107007727026939... | {"http://arxiv.org/abs/2412.12306v1": 0.9776857495307922, "http://arxiv.org/abs/2411.19795v1": 0.9472987055778503, "http://arxiv.org/abs/2411.12888v1": 0.9459424018859863, "http://arxiv.org/abs/2408.15772v1": 0.9438778162002563, "http://arxiv.org/abs/2409.03386v1": 0.9375158548355103, "http://arxiv.org/abs/2412.01165v2... | 0.977686 | 0.940189 |
http://arxiv.org/abs/2502.17442v2 | 2024-12-30T00:00:00 | Thinking Before Running! Efficient Code Generation with Thorough Exploration and Optimal Refinement | Xiaoqing Zhang; Yuhan Liu; Flood Sung; Xiuying Chen; Shuo Shang; Rui Yan | Code generation is crucial in software engineering for automating the coding process efficiently. While test-time computation methods show promise, they suffer from high latency due to multiple computation rounds. To overcome this, we introduce \textbf{ThinkCoder}, a framework that combines thorough exploration with op... | cs.SE; cs.AI | 0 | Random | [
-1.4378145933151245,
-1.0089763402938843,
-0.8467238545417786,
-0.6235949397087097,
-0.5082281827926636,
-1.2634028196334839,
-0.5601481199264526,
1.274387001991272,
0.042650748044252396,
-0.6186084747314453,
-0.7139586806297302,
0.3205374479293823,
1.2263833284378052,
0.16966073215007782,... | [
-0.1440812349319458,
0.4286888539791107,
-0.909812867641449,
0.041600294411182404,
-0.605337381362915,
-0.4424717426300049,
-0.18986016511917114,
-0.05093121528625488,
0.12928949296474457,
0.09100407361984253,
0.2567335367202759,
-0.7597620487213135,
0.174485981464386,
-0.23941290378570557... | {"http://arxiv.org/abs/2410.05605v2": 0.957175612449646, "http://arxiv.org/abs/2411.05010v2": 0.9568464756011963, "http://arxiv.org/abs/2412.15118v2": 0.9555341005325317, "http://arxiv.org/abs/2412.17264v1": 0.9519795179367065, "http://arxiv.org/abs/2410.10209v4": 0.9498131275177002, "http://arxiv.org/abs/2409.09584v1"... | 0.957176 | 0.951035 |
http://arxiv.org/abs/2412.20867v2 | 2024-12-30T00:00:00 | Holistic Construction Automation with Modular Robots: From High-Level Task Specification to Execution | Jonathan Külz; Michael Terzer; Marco Magri; Andrea Giusti; Matthias Althoff | In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices. This work proposes a holistic framework for construction task specification, optimization of robot morphol... | cs.RO; cs.AI; cs.HC | 0 | Random | [
-0.6275779008865356,
-0.645402729511261,
0.46022459864616394,
0.09598991274833679,
0.7906284928321838,
0.011085119098424911,
-1.0189964771270752,
0.06980034708976746,
0.06568493694067001,
-0.6277938485145569,
-0.851976215839386,
0.31969356536865234,
-0.375776469707489,
-1.50641667842865,
... | [
-0.20299960672855377,
0.129722461104393,
-0.16544209420681,
0.3333854377269745,
0.1532391905784607,
-0.14865419268608093,
-0.547741174697876,
-0.1985476315021515,
0.04266062751412392,
-0.4524194598197937,
-0.15023013949394226,
0.1671384871006012,
-0.7250229716300964,
-0.2947773039340973,
... | {"http://arxiv.org/abs/2409.03439v1": 0.9348006248474121, "http://arxiv.org/abs/2412.11275v1": 0.9340030550956726, "http://arxiv.org/abs/2411.16511v1": 0.9331945776939392, "http://arxiv.org/abs/2412.00147v1": 0.9303979873657227, "http://arxiv.org/abs/2411.09623v2": 0.9254232048988342, "http://arxiv.org/abs/2411.13156v1... | 0.934801 | 0.927327 |
http://arxiv.org/abs/2412.20631v2 | 2024-12-30T00:00:00 | Slow Perception: Let's Perceive Geometric Figures Step-by-step | Haoran Wei; Youyang Yin; Yumeng Li; Jia Wang; Liang Zhao; Jianjian Sun; Zheng Ge; Xiangyu Zhang; Daxin Jiang | Recently, "visual o1" began to enter people's vision, with expectations that this slow-thinking design can solve visual reasoning tasks, especially geometric math problems. However, the reality is that current LVLMs (Large Vision Language Models) can hardly even accurately copy a geometric figure, let alone truly under... | cs.CV | 0 | Random | [
-1.2684757709503174,
-0.13689477741718292,
1.2679997682571411,
-0.7586608529090881,
1.0745471715927124,
-0.08914010226726532,
-0.05881877988576889,
0.01044307742267847,
0.141314297914505,
-1.5470274686813354,
-0.3471299111843109,
0.7734963893890381,
0.9377888441085815,
-0.01393693313002586... | [
-0.27067291736602783,
0.6733584403991699,
-0.1228870302438736,
-0.02000322937965393,
0.3443463146686554,
-0.2030121088027954,
0.43123361468315125,
0.27789679169654846,
-0.43223896622657776,
-0.9496742486953735,
-0.12200146913528442,
0.2792312502861023,
0.08978937566280365,
-0.4407780468463... | {"http://arxiv.org/abs/2408.08313v4": 0.9342842698097229, "http://arxiv.org/abs/2409.08202v2": 0.9324473738670349, "http://arxiv.org/abs/2406.11327v2": 0.9288808703422546, "http://arxiv.org/abs/2303.02260v2": 0.9230107069015503, "http://arxiv.org/abs/2410.17255v1": 0.9225118160247803, "http://arxiv.org/abs/2401.09742v2... | 0.934284 | 0.924616 |
http://arxiv.org/abs/2412.20724v2 | 2024-12-30T00:00:00 | Soft Diamond Regularizers for Deep Learning | Olaoluwa Adigun; Bart Kosko | This chapter presents the new family of soft diamond synaptic regularizers based on thick-tailed symmetric alpha stable $S{\alpha}S$ probability bell curves. These new parametrized weight priors improved deep-learning performance on image and language-translation test sets and increased the sparsity of the trained weig... | stat.ML; cs.LG | 0 | Random | [
-0.5392370223999023,
-0.6448475122451782,
0.15115590393543243,
-0.1738937497138977,
-0.2278512716293335,
-0.4119656980037689,
0.419779896736145,
0.10521454364061356,
-0.8934746384620667,
-0.7277818322181702,
-0.18456357717514038,
1.0747538805007935,
1.0309467315673828,
0.49679234623908997,... | [
0.3861738443374634,
1.0041139125823975,
-0.6858350038528442,
-0.003328576683998108,
-0.13934265077114105,
0.718166172504425,
0.437541127204895,
-0.65057373046875,
-0.5503775477409363,
-0.3047558665275574,
0.35972023010253906,
0.04454262554645538,
0.4138229489326477,
-0.02129349112510681,
... | {"http://arxiv.org/abs/2402.16184v1": 0.9397465586662292, "http://arxiv.org/abs/2411.02001v2": 0.937419056892395, "http://arxiv.org/abs/2303.03382v1": 0.937355101108551, "http://arxiv.org/abs/2408.09966v2": 0.9357544183731079, "http://arxiv.org/abs/2409.18850v2": 0.9343118667602539, "http://arxiv.org/abs/2410.04234v2":... | 0.939747 | 0.934644 |
http://arxiv.org/abs/2412.21203v1 | 2024-12-30T00:00:00 | SoS Certificates for Sparse Singular Values and Their Applications: Robust Statistics, Subspace Distortion, and More | Ilias Diakonikolas; Samuel B. Hopkins; Ankit Pensia; Stefan Tiegel | We study $\textit{sparse singular value certificates}$ for random rectangular matrices. If $M$ is an $n \times d$ matrix with independent Gaussian entries, we give a new family of polynomial-time algorithms which can certify upper bounds on the maximum of $\|M u\|$, where $u$ is a unit vector with at most $\eta n$ nonz... | cs.DS; cs.LG | 0 | Random | [
-0.9633475542068481,
0.20916499197483063,
0.23714818060398102,
-0.06159424036741257,
-1.869304895401001,
-0.3101504147052765,
-0.44700732827186584,
0.5053847432136536,
0.17410966753959656,
-0.06879560649394989,
0.15701813995838165,
1.0901339054107666,
-0.38116204738616943,
0.05931518599390... | [
0.7072764039039612,
0.6929879784584045,
-0.36643481254577637,
0.48936089873313904,
-1.00557541847229,
-0.2909485697746277,
0.06563291698694229,
-0.31000134348869324,
-0.056248679757118225,
-0.4145454168319702,
0.30325162410736084,
0.1944955438375473,
-0.12751032412052155,
0.179785400629043... | {"http://arxiv.org/abs/2405.01517v2": 0.9565845131874084, "http://arxiv.org/abs/2410.21194v1": 0.9522071480751038, "http://arxiv.org/abs/2410.23937v1": 0.9469083547592163, "http://arxiv.org/abs/2406.02502v1": 0.9463530778884888, "http://arxiv.org/abs/2410.05412v1": 0.9436435103416443, "http://arxiv.org/abs/2405.05373v1... | 0.956585 | 0.945059 |
http://arxiv.org/abs/2412.21206v2 | 2024-12-30T00:00:00 | PERSE: Personalized 3D Generative Avatars from A Single Portrait | Hyunsoo Cha; Inhee Lee; Hanbyul Joo | We present PERSE, a method for building a personalized 3D generative avatar from a reference portrait. Our avatar enables facial attribute editing in a continuous and disentangled latent space to control each facial attribute, while preserving the individual's identity. To achieve this, our method begins by synthesizin... | cs.CV | 0 | Random | [
-1.1229678392410278,
0.053661152720451355,
-0.2982447147369385,
-0.925674557685852,
0.29002293944358826,
0.3439350426197052,
1.4530200958251953,
-0.8258229494094849,
0.4591985046863556,
-1.3120242357254028,
-0.0582180954515934,
1.5499731302261353,
1.0412133932113647,
0.5028967261314392,
... | [
0.11274182796478271,
0.6132569909095764,
-0.12062951177358627,
-0.19730088114738464,
-0.14546263217926025,
0.025976218283176422,
0.7507845759391785,
-0.9196747541427612,
0.2591995894908905,
-0.6717044711112976,
0.9962189793586731,
1.0023283958435059,
0.05555177107453346,
0.0685601755976677... | {"http://arxiv.org/abs/2408.10588v2": 0.9697195291519165, "http://arxiv.org/abs/2410.07160v1": 0.9657524824142456, "http://arxiv.org/abs/2412.10209v2": 0.9634004831314087, "http://arxiv.org/abs/2409.17145v1": 0.9614897966384888, "http://arxiv.org/abs/2409.13591v1": 0.9603357911109924, "http://arxiv.org/abs/2410.07971v1... | 0.96972 | 0.959306 |
http://arxiv.org/abs/2412.20954v2 | 2024-12-30T00:00:00 | AGON: Automated Design Framework for Customizing Processors from ISA Documents | Chongxiao Li; Di Huang; Pengwei Jin; Tianyun Ma; Husheng Han; Shuyao Cheng; Yifan Hao; Yongwei Zhao; Guanglin Xu; Zidong Du; Rui Zhang; Xiaqing Li; Yuanbo Wen; Xing Hu; Qi Guo | Customized processors are attractive solutions for vast domain-specific applications due to their high energy efficiency. However, designing a processor in traditional flows is time-consuming and expensive. To address this, researchers have explored methods including the use of agile development tools like Chisel or Sp... | cs.AR | 0 | Random | [
-1.4361686706542969,
0.16378094255924225,
-0.9400569796562195,
0.0260637104511261,
-0.3351622521877289,
-0.5251564383506775,
-0.7290370464324951,
0.9054577946662903,
0.2536433935165405,
-0.559110164642334,
0.10544202476739883,
0.04457157850265503,
1.6800235509872437,
-0.1680486798286438,
... | [
0.5228443741798401,
0.7027140855789185,
-0.9249436259269714,
0.40101343393325806,
-0.3432348668575287,
0.22802942991256714,
-0.2774619460105896,
0.02797558903694153,
-0.07680784910917282,
-0.30736327171325684,
0.8561328053474426,
-0.3096995949745178,
0.28476962447166443,
0.0883982032537460... | {"http://arxiv.org/abs/2410.13079v1": 0.9457358717918396, "http://arxiv.org/abs/2308.11048v1": 0.9433983564376831, "http://arxiv.org/abs/2305.08435v1": 0.9419414401054382, "http://arxiv.org/abs/2312.16300v1": 0.9415031671524048, "http://arxiv.org/abs/2401.10249v1": 0.9401366114616394, "http://arxiv.org/abs/2308.07654v1... | 0.945736 | 0.940816 |
http://arxiv.org/abs/2412.20703v2 | 2024-12-30T00:00:00 | The Restricted Inverse Optimal Value Problem under Weighted Bottle-neck Hamming distance on trees | Qiao Zhang; Xiao Li; Xiucui Guan | We consider the Restricted Inverse Optimal Value Problem (RIOVSP) on trees under weighted bottleneck Hamming distance, denoted as (RIOVSPT$_{BH}$). The problem aims to minimize the total cost under weighted bottle-neck Hamming distance such that the length of the shortest root-leaf path of the tree is lower-bounded by ... | cs.DS; math.OC | 0 | Random | [
-1.2818125486373901,
-0.4331153929233551,
-0.7468991875648499,
-0.07371547073125839,
-0.39263659715652466,
-0.2928847372531891,
-1.6854909658432007,
0.9449682235717773,
-0.1164037361741066,
1.117336630821228,
-1.0723998546600342,
1.5208476781845093,
-1.0757089853286743,
-0.5490201115608215... | [
0.7301239967346191,
0.6922134160995483,
-0.900637686252594,
0.2092473804950714,
-0.10379751026630402,
0.160276859998703,
-0.802661120891571,
0.2794891595840454,
-0.3895135223865509,
0.2427050620317459,
0.07323132455348969,
0.1053352802991867,
-0.4690459668636322,
-0.2160061150789261,
0.0... | {"http://arxiv.org/abs/2411.06502v3": 0.9317708015441895, "http://arxiv.org/abs/2408.00899v1": 0.9270172715187073, "http://arxiv.org/abs/2411.17271v1": 0.9265482425689697, "http://arxiv.org/abs/2408.14406v1": 0.9265219569206238, "http://arxiv.org/abs/2406.19709v2": 0.926338791847229, "http://arxiv.org/abs/2404.19164v1"... | 0.931771 | 0.926008 |
http://arxiv.org/abs/2412.21077v1 | 2024-12-30T00:00:00 | 3GPP Evolution from 5G to 6G: A 10-Year Retrospective | Xingqin Lin | The 3rd Generation Partnership Project (3GPP) evolution of mobile communication technologies from 5G to 6G has been a transformative journey spanning a decade, shaped by six releases from Release 15 to Release 20. This article provides a retrospective of this evolution, highlighting the technical advancements, challeng... | cs.NI | 0 | Random | [
0.02639511600136757,
0.2912142276763916,
-0.7575780153274536,
0.20067530870437622,
0.4821408987045288,
0.11176711320877075,
-0.07522276788949966,
-0.9471461176872253,
0.8156861066818237,
0.600020706653595,
-0.1198720782995224,
-0.05116674676537514,
0.2904042899608612,
-0.02129044383764267,... | [
0.5883002281188965,
1.2133268117904663,
-0.8234602808952332,
0.4270023703575134,
0.29489052295684814,
0.0614253394305706,
0.12330631166696548,
-0.9110599160194397,
0.24965736269950867,
0.30767232179641724,
0.5942354798316956,
-0.5472240447998047,
-0.12795984745025635,
-0.06820020079612732,... | {"http://arxiv.org/abs/2412.13295v3": 0.9698514938354492, "http://arxiv.org/abs/2412.07029v3": 0.9565427899360657, "http://arxiv.org/abs/2411.18836v1": 0.9534623622894287, "http://arxiv.org/abs/2412.14538v4": 0.9533104300498962, "http://arxiv.org/abs/2411.18435v1": 0.9515353441238403, "http://arxiv.org/abs/2411.09959v1... | 0.969851 | 0.951759 |
http://arxiv.org/abs/2501.00113v1 | 2024-12-30T00:00:00 | AltGen: AI-Driven Alt Text Generation for Enhancing EPUB Accessibility | Yixian Shen; Hang Zhang; Yanxin Shen; Lun Wang; Chuanqi Shi; Shaoshuai Du; Yiyi Tao | Digital accessibility is a cornerstone of inclusive content delivery, yet many EPUB files fail to meet fundamental accessibility standards, particularly in providing descriptive alt text for images. Alt text plays a critical role in enabling visually impaired users to understand visual content through assistive technol... | cs.AI | 0 | Random | [
-0.526833176612854,
0.2643674314022064,
0.4813399910926819,
-0.9927875995635986,
-0.5077046155929565,
-0.023023873567581177,
1.284053087234497,
-0.015179875306785107,
-0.6795172095298767,
-1.4767224788665771,
0.015341125428676605,
0.8605639934539795,
0.6338695883750916,
0.900310754776001,
... | [
0.3816336393356323,
0.4483923017978668,
0.09298244118690491,
0.21909788250923157,
-0.6348052024841309,
-0.3258059024810791,
0.6398729681968689,
-0.2606227993965149,
-0.019015999510884285,
-0.7423799633979797,
0.7878009080886841,
-0.04914183169603348,
0.01202477514743805,
-0.105803757905960... | {"http://arxiv.org/abs/2409.10695v2": 0.9496185779571533, "http://arxiv.org/abs/2411.12293v1": 0.9408379793167114, "http://arxiv.org/abs/2310.02992v3": 0.9372219443321228, "http://arxiv.org/abs/2311.03054v5": 0.9371564984321594, "http://arxiv.org/abs/2409.19051v1": 0.9365747570991516, "http://arxiv.org/abs/2307.05222v2... | 0.949619 | 0.937387 |
http://arxiv.org/abs/2412.20733v1 | 2024-12-30T00:00:00 | Towards nation-wide analytical healthcare infrastructures: A privacy-preserving augmented knee rehabilitation case study | Boris Bačić; Claudiu Vasile; Chengwei Feng; Marian G. Ciucă | The purpose of this paper is to contribute towards the near-future privacy-preserving big data analytical healthcare platforms, capable of processing streamed or uploaded timeseries data or videos from patients. The experimental work includes a real-life knee rehabilitation video dataset capturing a set of exercises fr... | cs.CV; cs.AI; cs.CY; cs.MM | 0 | Random | [
-0.34431135654449463,
0.5400949120521545,
-1.3327549695968628,
-0.016003631055355072,
-0.7831954956054688,
0.5906948447227478,
0.20161768794059753,
-1.0596431493759155,
-0.4269293248653412,
-0.5984964966773987,
0.8864486813545227,
1.6916183233261108,
-1.364222526550293,
0.5139694213867188,... | [
0.6121408343315125,
0.701910674571991,
-0.9587160348892212,
-0.09082456678152084,
-0.007514718919992447,
0.07306890189647675,
0.12750869989395142,
-0.1738552302122116,
-0.4221406877040863,
-0.15405522286891937,
0.8787267208099365,
0.2997913360595703,
-0.4329584538936615,
0.1764113008975982... | {"http://arxiv.org/abs/2312.04470v6": 0.9414041638374329, "http://arxiv.org/abs/2406.07558v2": 0.9344903826713562, "http://arxiv.org/abs/2411.02099v2": 0.930054783821106, "http://arxiv.org/abs/2407.17021v1": 0.9245487451553345, "http://arxiv.org/abs/2409.00701v1": 0.9243585467338562, "http://arxiv.org/abs/2412.06759v3"... | 0.941404 | 0.92482 |
http://arxiv.org/abs/2412.21178v1 | 2024-12-30T00:00:00 | Two-component spatiotemporal template for activation-inhibition of speech in ECoG | Eric Easthope | I compute the average trial-by-trial power of band-limited speech activity across epochs of multi-channel high-density electrocorticography (ECoG) recorded from multiple subjects during a consonant-vowel speaking task. I show that previously seen anti-correlations of average beta frequency activity (12-35 Hz) to high-f... | q-bio.NC; cs.CL; cs.LG; eess.AS; eess.SP | 0 | Random | [
0.5587162971496582,
0.44233667850494385,
0.3722975254058838,
-0.5922066569328308,
0.17229364812374115,
-0.057030607014894485,
0.8973150849342346,
-0.3103635311126709,
-0.06422105431556702,
-0.11000001430511475,
1.1848838329315186,
0.08967748284339905,
0.5209894776344299,
0.7047982811927795... | [
0.4048234224319458,
1.1359100341796875,
-0.10879850387573242,
0.27226167917251587,
-0.37372443079948425,
0.3804522752761841,
0.2868838906288147,
-0.4662878215312958,
-0.1616842895746231,
-0.19351990520954132,
1.3983420133590698,
0.24169154465198517,
-0.04993973672389984,
0.6499506831169128... | {"http://arxiv.org/abs/2412.16773v1": 0.9125908017158508, "http://arxiv.org/abs/2306.13429v2": 0.9087517857551575, "http://arxiv.org/abs/2412.12215v1": 0.9047858715057373, "http://arxiv.org/abs/2409.00035v3": 0.9046869874000549, "http://arxiv.org/abs/2305.14369v1": 0.8989126086235046, "http://arxiv.org/abs/2408.08877v1... | 0.912591 | 0.901692 |
http://arxiv.org/abs/2412.21154v1 | 2024-12-30T00:00:00 | Aviary: training language agents on challenging scientific tasks | Siddharth Narayanan; James D. Braza; Ryan-Rhys Griffiths; Manu Ponnapati; Albert Bou; Jon Laurent; Ori Kabeli; Geemi Wellawatte; Sam Cox; Samuel G. Rodriques; Andrew D. White | Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and experimentation. Language agents are promising for automating intellectual tasks in science because they can interact with tools via natural lang... | cs.AI; cs.CL; cs.LG | 0 | Random | [
-0.827422559261322,
0.03235597163438797,
0.10961923748254776,
-0.3426520824432373,
0.1878068745136261,
-1.4865256547927856,
-1.0579732656478882,
0.9186269640922546,
-0.5662246942520142,
0.6008253693580627,
-0.5067111849784851,
0.6581762433052063,
0.27842894196510315,
0.09210765361785889,
... | [
0.18862798810005188,
0.5521795749664307,
-0.39056941866874695,
-0.022581040859222412,
-0.34555113315582275,
-0.6734508872032166,
0.3225550949573517,
0.011159674264490604,
-0.6459538340568542,
0.16503030061721802,
-0.27184098958969116,
-0.8663793802261353,
0.052169885486364365,
0.2712514102... | {"http://arxiv.org/abs/2310.01557v5": 0.9353598952293396, "http://arxiv.org/abs/2405.17631v3": 0.9312764406204224, "http://arxiv.org/abs/2405.16450v3": 0.9291067719459534, "http://arxiv.org/abs/2410.05434v1": 0.9282113313674927, "http://arxiv.org/abs/2501.10385v2": 0.9276291131973267, "http://arxiv.org/abs/2410.17238v1... | 0.93536 | 0.927566 |
http://arxiv.org/abs/2501.00162v1 | 2024-12-30T00:00:00 | Class-based Subset Selection for Transfer Learning under Extreme Label Shift | Akul Goyal; Carl Edwards | Existing work within transfer learning often follows a two-step process -- pre-training over a large-scale source domain and then finetuning over limited samples from the target domain. Yet, despite its popularity, this methodology has been shown to suffer in the presence of distributional shift -- specifically when th... | cs.LG; cs.AI | 0 | Random | [
-1.426955223083496,
-0.7904430627822876,
-0.15172138810157776,
-0.647331714630127,
-0.83912593126297,
-0.1817750483751297,
0.615250289440155,
0.27733558416366577,
-0.8882576823234558,
-0.2882918119430542,
-0.11821107566356659,
1.2773488759994507,
0.3654620945453644,
0.715988278388977,
0.... | [
0.38358253240585327,
0.44530072808265686,
-0.5704368948936462,
-0.3775254189968109,
-0.8092077970504761,
-0.06093346327543259,
0.5775608420372009,
-0.47133785486221313,
-0.10161716490983963,
0.2663588523864746,
0.04710427671670914,
0.345345139503479,
0.055000077933073044,
-0.04691445082426... | {"http://arxiv.org/abs/2312.17263v1": 0.9367809891700745, "http://arxiv.org/abs/2412.16255v1": 0.9291190505027771, "http://arxiv.org/abs/2402.04416v3": 0.9285532236099243, "http://arxiv.org/abs/2312.15923v1": 0.9279918670654297, "http://arxiv.org/abs/2205.13577v2": 0.9278104305267334, "http://arxiv.org/abs/2312.16467v1... | 0.936781 | 0.927088 |
http://arxiv.org/abs/2412.21156v1 | 2024-12-30T00:00:00 | Unified dimensionality reduction techniques in chronic liver disease detection | Anand Karna; Naina Khan; Rahul Rauniyar; Prashant Giridhar Shambharkar | Globally, chronic liver disease continues to be a major health concern that requires precise predictive models for prompt detection and treatment. Using the Indian Liver Patient Dataset (ILPD) from the University of California at Irvine's UCI Machine Learning Repository, a number of machine learning algorithms are inve... | cs.LG | 0 | Random | [
-1.1271538734436035,
-0.5379607677459717,
0.1517535001039505,
-0.5782830119132996,
-0.24053232371807098,
0.37416329979896545,
1.5193262100219727,
-0.6658631563186646,
0.7225799560546875,
0.8188439607620239,
0.07413235306739807,
0.6734636425971985,
0.20459912717342377,
-0.12794466316699982,... | [
0.10460406541824341,
0.2480417639017105,
-0.43254613876342773,
0.09268002212047577,
-0.3948511779308319,
-0.03366132825613022,
0.31411826610565186,
-0.38693132996559143,
0.26241251826286316,
-0.08499963581562042,
0.893568217754364,
0.126825213432312,
-0.33814728260040283,
0.292768895626068... | {"http://arxiv.org/abs/2407.19125v1": 0.9156901836395264, "http://arxiv.org/abs/2411.05697v2": 0.9121902585029602, "http://arxiv.org/abs/2407.15611v1": 0.911617636680603, "http://arxiv.org/abs/2407.14631v2": 0.9110868573188782, "http://arxiv.org/abs/2401.00883v1": 0.9109739065170288, "http://arxiv.org/abs/2409.00155v1"... | 0.91569 | 0.910927 |
http://arxiv.org/abs/2412.20660v1 | 2024-12-30T00:00:00 | Energy Efficient LoRaWAN in LEO Satellites | Muskan Shergill; Zach Thompson; Guanqun Song; Ting Zhu | LPWAN service's inexpensive cost and long range capabilities make it a promising addition and countless satellite companies have started taking advantage of this technology to connect IoT users across the globe. However, LEO satellites have the unique challenge of using rechargeable batteries and green solar energy to ... | cs.ET; eess.SP | 0 | Random | [
-0.7745304703712463,
-0.41949567198753357,
-0.7642818093299866,
-0.5684882402420044,
0.36560070514678955,
-0.8856384754180908,
-0.15440945327281952,
-0.6266552209854126,
0.14551740884780884,
0.4869118332862854,
-0.4966832101345062,
0.08309462666511536,
0.3756464719772339,
-0.44029358029365... | [
0.09853128343820572,
0.45709991455078125,
-0.389244943857193,
0.48270484805107117,
-0.05013079196214676,
-0.20820674300193787,
-0.02600651979446411,
-0.4010424017906189,
-0.34502390027046204,
0.7049593329429626,
0.6590782999992371,
-0.0665384829044342,
0.2816946506500244,
-0.24844506382942... | {"http://arxiv.org/abs/2409.20408v1": 0.9458630681037903, "http://arxiv.org/abs/2410.09927v1": 0.9386003017425537, "http://arxiv.org/abs/2408.04908v2": 0.9377284049987793, "http://arxiv.org/abs/2412.04563v1": 0.9301450848579407, "http://arxiv.org/abs/2412.19549v1": 0.9269119501113892, "http://arxiv.org/abs/2409.04002v1... | 0.945863 | 0.929619 |
http://arxiv.org/abs/2412.20722v2 | 2024-12-30T00:00:00 | Improving Acoustic Scene Classification in Low-Resource Conditions | Zhi Chen; Yun-Fei Shao; Yong Ma; Mingsheng Wei; Le Zhang; Wei-Qiang Zhang | Acoustic Scene Classification (ASC) identifies an environment based on an audio signal. This paper explores ASC in low-resource conditions and proposes a novel model, DS-FlexiNet, which combines depthwise separable convolutions from MobileNetV2 with ResNet-inspired residual connections for a balance of efficiency and a... | eess.AS; cs.SD | 0 | Random | [
-0.2828010320663452,
-1.0153216123580933,
-0.3578605651855469,
-0.9816123247146606,
-0.38738715648651123,
0.4461005926132202,
0.37176546454429626,
0.2981662452220917,
0.018101897090673447,
-1.4558316469192505,
0.30630815029144287,
0.9736943244934082,
1.3169684410095215,
0.5568463206291199,... | [
0.365939736366272,
0.08198308944702148,
-0.12111657112836838,
0.1203707903623581,
-0.7041565179824829,
-0.12940989434719086,
0.07608680427074432,
0.03836136311292648,
-0.027638740837574005,
-1.0118783712387085,
0.8297722935676575,
0.16193635761737823,
0.12965835630893707,
0.196035668253898... | {"http://arxiv.org/abs/2412.17121v1": 0.943067729473114, "http://arxiv.org/abs/2410.22803v1": 0.9374189972877502, "http://arxiv.org/abs/2410.18322v3": 0.9359433054924011, "http://arxiv.org/abs/2412.17212v1": 0.9352785348892212, "http://arxiv.org/abs/2408.16532v3": 0.9338043928146362, "http://arxiv.org/abs/2409.01352v1"... | 0.943068 | 0.93084 |
http://arxiv.org/abs/2412.20864v1 | 2024-12-30T00:00:00 | Enhancing Annotated Bibliography Generation with LLM Ensembles | Sergio Bermejo | This work proposes a novel approach to enhancing annotated bibliography generation through Large Language Model (LLM) ensembles. In particular, multiple LLMs in different roles -- controllable text generation, evaluation, and summarization -- are introduced and validated using a systematic methodology to enhance model ... | cs.CL; cs.AI; cs.LG | 0 | Random | [
-1.2915481328964233,
0.7193168997764587,
-0.2872622311115265,
-0.07285963743925095,
-0.8176140785217285,
-0.7894628047943115,
1.4300912618637085,
0.2924216687679291,
-1.196804165840149,
0.32480359077453613,
0.06651584804058075,
0.02751418948173523,
-0.04162188246846199,
1.202028512954712,
... | [
0.05597173422574997,
0.7277875542640686,
-0.4552803039550781,
0.07024084776639938,
-1.1985135078430176,
-0.19316576421260834,
0.7157294154167175,
-0.2504725754261017,
0.2523193657398224,
0.3059849441051483,
0.9359729886054993,
0.07044520229101181,
0.02216595783829689,
0.17831602692604065,
... | {"http://arxiv.org/abs/2411.07127v2": 0.9499309062957764, "http://arxiv.org/abs/2409.18812v1": 0.9398287534713745, "http://arxiv.org/abs/2401.10040v1": 0.9394088983535767, "http://arxiv.org/abs/2407.12857v2": 0.9387780427932739, "http://arxiv.org/abs/2411.02429v1": 0.9380923509597778, "http://arxiv.org/abs/2406.11370v2... | 0.949931 | 0.938359 |
http://arxiv.org/abs/2412.21001v1 | 2024-12-30T00:00:00 | LEASE: Offline Preference-based Reinforcement Learning with High Sample Efficiency | Xiao-Yin Liu; Guotao Li; Xiao-Hu Zhou; Zeng-Guang Hou | Offline preference-based reinforcement learning (PbRL) provides an effective way to overcome the challenges of designing reward and the high costs of online interaction. However, since labeling preference needs real-time human feedback, acquiring sufficient preference labels is challenging. To solve this, this paper pr... | cs.LG; cs.AI | 0 | Random | [
-1.4348284006118774,
-0.46257996559143066,
-0.7321759462356567,
-0.6463472843170166,
-0.5814778208732605,
0.7672931551933289,
-0.07167628407478333,
0.12342488765716553,
-0.31524306535720825,
0.040562182664871216,
-0.9030268788337708,
1.1205570697784424,
0.23486573994159698,
0.2830146849155... | [
-0.3606773614883423,
0.5055792927742004,
-1.1460926532745361,
-0.07098712772130966,
-0.6015710830688477,
-0.1305370032787323,
-0.042830951511859894,
0.16260826587677002,
-0.5423529744148254,
0.4538722038269043,
0.05287300795316696,
0.14045709371566772,
-0.03836661949753761,
0.1436645984649... | {"http://arxiv.org/abs/2406.18450v2": 0.9666278958320618, "http://arxiv.org/abs/2401.00330v3": 0.9615424275398254, "http://arxiv.org/abs/2302.01667v1": 0.955273449420929, "http://arxiv.org/abs/2303.00957v1": 0.9511652588844299, "http://arxiv.org/abs/2210.09151v2": 0.948248565196991, "http://arxiv.org/abs/2308.02585v3":... | 0.966628 | 0.949965 |
http://arxiv.org/abs/2412.20772v2 | 2024-12-30T00:00:00 | Large Language Model Enabled Multi-Task Physical Layer Network | Tianyue Zheng; Linglong Dai | The advance of Artificial Intelligence (AI) is continuously reshaping the future 6G wireless communications. Particularly, the development of Large Language Models (LLMs) offers a promising approach to effectively improve the performance and generalization of AI in different physical-layer (PHY) tasks. However, most ex... | cs.IT; eess.SP; math.IT | 0 | Random | [
0.38076144456863403,
0.07258668541908264,
-0.9261846542358398,
-1.1898235082626343,
-0.30792903900146484,
0.2533678114414215,
-0.3942529261112213,
-0.3670537769794464,
0.7857975959777832,
0.0997452437877655,
-0.033118437975645065,
-0.6953002214431763,
0.8999638557434082,
0.9746693968772888... | [
0.7398466467857361,
0.7918695211410522,
-0.43151718378067017,
0.18265315890312195,
-0.4553881883621216,
0.4901232123374939,
0.1343384087085724,
-0.6284840703010559,
0.11079872399568558,
0.013508789241313934,
0.4575916528701782,
-0.07789933681488037,
0.3041137456893921,
0.3303251266479492,
... | {"http://arxiv.org/abs/2410.21351v1": 0.9498472809791565, "http://arxiv.org/abs/2409.04302v1": 0.9467506408691406, "http://arxiv.org/abs/2410.01597v1": 0.9401895999908447, "http://arxiv.org/abs/2408.08765v3": 0.9373282194137573, "http://arxiv.org/abs/2412.09839v3": 0.936945915222168, "http://arxiv.org/abs/2411.10385v1"... | 0.949847 | 0.937532 |
http://arxiv.org/abs/2501.00175v1 | 2024-12-30T00:00:00 | Strategies and Challenges of Timestamp Tampering for Improved Digital Forensic Event Reconstruction (extended version) | Céline Vanini; Jan Gruber; Christopher Hargreaves; Zinaida Benenson; Felix Freiling; Frank Breitinger | Timestamps play a pivotal role in digital forensic event reconstruction, but due to their non-essential nature, tampering or manipulation of timestamps is possible by users in multiple ways, even on running systems. This has a significant effect on the reliability of the results from applying a timeline analysis as par... | cs.CR | 0 | Random | [
-0.32660430669784546,
-0.16544046998023987,
-0.3116948902606964,
-0.6789272427558899,
-0.42381569743156433,
-0.9923641085624695,
1.3428657054901123,
0.2544938921928406,
0.16116803884506226,
-1.0349701642990112,
-1.2086292505264282,
0.05843965709209442,
0.179973304271698,
0.0027872063219547... | [
0.27602851390838623,
0.5169728398323059,
-0.6342038512229919,
0.20624148845672607,
-0.6636589765548706,
-0.6173433661460876,
0.5881783962249756,
0.08105383068323135,
-0.30057647824287415,
-0.2627002000808716,
0.22088512778282166,
-0.41993772983551025,
-0.008235666900873184,
-0.005350051447... | {"http://arxiv.org/abs/2412.12814v1": 0.9564651846885681, "http://arxiv.org/abs/2312.11292v2": 0.942077100276947, "http://arxiv.org/abs/2410.12605v1": 0.9304414987564087, "http://arxiv.org/abs/2309.00735v1": 0.9284871816635132, "http://arxiv.org/abs/2309.05537v1": 0.9255605936050415, "http://arxiv.org/abs/2312.15215v1"... | 0.956465 | 0.925771 |
http://arxiv.org/abs/2412.21042v1 | 2024-12-30T00:00:00 | Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration | Wanglong Lu; Jikai Wang; Tao Wang; Kaihao Zhang; Xianta Jiang; Hanli Zhao | Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information retrievable from the degraded images. Prior knowledge-based methods, leveraging geometric priors and facial features, have led to advancements ... | cs.CV; cs.MM; 68U10; I.4.3; I.4.4; I.4.5; I.4.9 | 0 | Random | [
-0.4059610962867737,
0.37679195404052734,
-0.26690980792045593,
-0.8283545970916748,
-0.17028507590293884,
0.6336730718612671,
0.6550208926200867,
-0.7442944049835205,
0.28026148676872253,
-1.4342195987701416,
-0.28406772017478943,
1.6979999542236328,
0.4056076407432556,
0.5406932830810547... | [
0.01471826434135437,
0.5391666293144226,
-0.12286567687988281,
-0.01450406014919281,
-0.2094486951828003,
0.06447805464267731,
0.7603461146354675,
-0.6215783953666687,
0.4318389594554901,
-0.6821030974388123,
0.7908461689949036,
0.6276339292526245,
-0.1846224069595337,
0.10774688422679901,... | {"http://arxiv.org/abs/2312.17234v1": 0.9600460529327393, "http://arxiv.org/abs/2412.19853v2": 0.9419262409210205, "http://arxiv.org/abs/2312.17161v2": 0.941055178642273, "http://arxiv.org/abs/2310.02401v2": 0.9394016265869141, "http://arxiv.org/abs/2409.08258v1": 0.9359583258628845, "http://arxiv.org/abs/2308.07314v2"... | 0.960046 | 0.938426 |
http://arxiv.org/abs/2412.20996v1 | 2024-12-30T00:00:00 | Plug-and-Play Training Framework for Preference Optimization | Jingyuan Ma; Rui Li; Zheng Li; Lei Sha; Zhifang Sui | Recently, preference optimization methods such as DPO have significantly enhanced large language models (LLMs) in wide tasks including dialogue and question-answering. However, current methods fail to account for the varying difficulty levels of training samples during preference optimization, leading to mediocre perfo... | cs.CL | 0 | Random | [
-0.7514705657958984,
-0.1549024134874344,
0.3415828049182892,
-1.2493470907211304,
0.18272794783115387,
-0.4661838412284851,
0.8170808553695679,
0.7472456097602844,
-1.2983813285827637,
-0.9866324663162231,
-0.3569360673427582,
0.4611743688583374,
0.8517851233482361,
0.6850259304046631,
... | [
-0.17350170016288757,
0.6743133068084717,
-0.49792468547821045,
-0.3878881633281708,
-0.9308657646179199,
-0.0750410258769989,
0.23939143121242523,
0.17410123348236084,
-0.5683516263961792,
-0.06374751031398773,
0.5589215159416199,
-0.0072900038212537766,
-0.20208004117012024,
0.0588047392... | {"http://arxiv.org/abs/2406.07327v2": 0.9514389038085938, "http://arxiv.org/abs/2408.07471v4": 0.9495068788528442, "http://arxiv.org/abs/2412.15282v1": 0.948628306388855, "http://arxiv.org/abs/2411.16345v2": 0.9455747008323669, "http://arxiv.org/abs/2310.13011v2": 0.9411901831626892, "http://arxiv.org/abs/2412.14516v1"... | 0.951439 | 0.943547 |
http://arxiv.org/abs/2501.01984v1 | 2024-12-30T00:00:00 | Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging | Atharva Divekar; Atharva Sonawane | The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS) through automated classification of healthy and unhealthy ultrasound frames. This report outlines our methodology for building a robust AI pipeline utilizin... | eess.IV; cs.AI; cs.CV; I.4.9 | 0 | Random | [
0.08188237249851227,
1.3494255542755127,
-0.5582031011581421,
-0.09036589413881302,
0.0006498992443084717,
0.33070531487464905,
1.3153201341629028,
-0.3034127354621887,
1.8476979732513428,
-1.3739417791366577,
-0.15629702806472778,
1.6260197162628174,
0.50840824842453,
0.2719202935695648,
... | [
0.8802042603492737,
1.139829158782959,
-0.37377673387527466,
0.01823589950799942,
-0.17157970368862152,
0.4136011004447937,
0.17359791696071625,
-0.22275985777378082,
0.42391231656074524,
-0.4902346730232239,
0.21164540946483612,
0.16474632918834686,
-0.047828108072280884,
0.23704032599925... | {"http://arxiv.org/abs/2411.15128v2": 0.929293692111969, "http://arxiv.org/abs/2411.03782v1": 0.9244832992553711, "http://arxiv.org/abs/2501.00053v1": 0.9242466688156128, "http://arxiv.org/abs/2401.09450v2": 0.9197317361831665, "http://arxiv.org/abs/2406.07558v2": 0.9168705940246582, "http://arxiv.org/abs/2406.11143v2"... | 0.929294 | 0.919281 |
http://arxiv.org/abs/2412.20680v1 | 2024-12-30T00:00:00 | Online Adaptive Platoon Control for Connected and Automated Vehicles via Physics Enhanced Residual Learning | Peng Zhang; Heye Huang; Hang Zhou; Haotian Shi; Keke Long; Xiaopeng Li | This paper introduces a physics enhanced residual learning (PERL) framework for connected and automated vehicle (CAV) platoon control, addressing the dynamics and unpredictability inherent to platoon systems. The framework first develops a physics-based controller to model vehicle dynamics, using driving speed as input... | cs.RO; cs.SY; eess.SY | 0 | Random | [
-1.22645103931427,
-0.6560837626457214,
-0.4612983465194702,
-0.4528222680091858,
0.7506271600723267,
-0.03176351636648178,
0.33072003722190857,
-0.7976948022842407,
-0.12338536977767944,
-0.07515609264373779,
-0.9939091801643372,
0.2986253499984741,
0.4905584752559662,
-1.2169021368026733... | [
0.0289919376373291,
0.2959674596786499,
-0.6788397431373596,
1.0225539207458496,
0.10618804395198822,
0.24860498309135437,
0.38566702604293823,
-0.4563848674297333,
-0.16030865907669067,
-0.08555649220943451,
-0.2986639142036438,
-0.5732825398445129,
-0.015711601823568344,
-0.2543269395828... | {"http://arxiv.org/abs/2412.15079v1": 0.9565922021865845, "http://arxiv.org/abs/2412.03874v1": 0.9528641700744629, "http://arxiv.org/abs/2410.23916v1": 0.9437425136566162, "http://arxiv.org/abs/2412.17118v1": 0.9419858455657959, "http://arxiv.org/abs/2410.23634v3": 0.9385353922843933, "http://arxiv.org/abs/2411.00107v2... | 0.956592 | 0.941307 |
http://arxiv.org/abs/2412.21063v2 | 2024-12-30T00:00:00 | Navigating Image Restoration with VAR's Distribution Alignment Prior | Siyang Wang; Feng Zhao | Generative models trained on extensive high-quality datasets effectively capture the structural and statistical properties of clean images, rendering them powerful priors for transforming degraded features into clean ones in image restoration. VAR, a novel image generative paradigm, surpasses diffusion models in genera... | cs.CV | 0 | Random | [
-0.20894373953342438,
-0.31228381395339966,
0.4547034800052643,
-0.1713767647743225,
-1.1609519720077515,
0.48825544118881226,
0.6845110654830933,
-0.49990037083625793,
0.28900423645973206,
-0.37852150201797485,
-0.29138749837875366,
1.4142186641693115,
0.4122292995452881,
-0.1273398846387... | [
0.5380064249038696,
0.3006788492202759,
0.5535144209861755,
0.19495567679405212,
-0.8417460918426514,
0.09680387377738953,
0.4315400719642639,
-0.5607798099517822,
0.29056715965270996,
-0.7117624878883362,
0.8217470645904541,
0.6567714214324951,
-0.2127285748720169,
-0.14663532376289368,
... | {"http://arxiv.org/abs/2312.17161v2": 0.9584769010543823, "http://arxiv.org/abs/2412.15032v2": 0.946677565574646, "http://arxiv.org/abs/2312.16486v2": 0.9436016082763672, "http://arxiv.org/abs/2412.19853v2": 0.9434511661529541, "http://arxiv.org/abs/2411.12072v1": 0.9434225559234619, "http://arxiv.org/abs/2401.00877v2"... | 0.958477 | 0.943875 |
http://arxiv.org/abs/2501.00167v1 | 2024-12-30T00:00:00 | On Functional Observability of Nonlinear Systems and the Design of Functional Observers with Assignable Error Dynamics | Costas Kravaris | This paper proposes a novel approach for designing functional observers for nonlinear systems, with linear error dynamics and assignable poles. Sufficient conditions for functional observability are first derived, leading to functional relationships between the Lie derivatives of the output to be estimated and the ones... | eess.SY; cs.SY | 0 | Random | [
-0.21681369841098785,
0.527778685092926,
0.01820925623178482,
0.6735406517982483,
0.6861277222633362,
-0.2995336353778839,
0.630062997341156,
-0.3485909700393677,
0.27902135252952576,
0.38305121660232544,
-2.175351858139038,
0.3811083436012268,
-1.0613222122192383,
-0.6540005207061768,
0... | [
0.24153228104114532,
0.7953053712844849,
-0.7260590195655823,
0.526684045791626,
0.07700540125370026,
0.20952197909355164,
-0.4804661273956299,
-0.3345138430595398,
0.35946863889694214,
0.029540587216615677,
0.2298860251903534,
0.16893857717514038,
-0.7003173232078552,
0.47130846977233887,... | {"http://arxiv.org/abs/2411.03011v1": 0.9138140678405762, "http://arxiv.org/abs/2412.19280v2": 0.911730170249939, "http://arxiv.org/abs/2410.12466v1": 0.9078763723373413, "http://arxiv.org/abs/2412.18492v1": 0.9059414863586426, "http://arxiv.org/abs/2412.02547v2": 0.904893159866333, "http://arxiv.org/abs/2402.04241v1":... | 0.913814 | 0.905064 |
http://arxiv.org/abs/2412.21059v2 | 2024-12-30T00:00:00 | VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation | Jiazheng Xu; Yu Huang; Jiale Cheng; Yuanming Yang; Jiajun Xu; Yuan Wang; Wenbo Duan; Shen Yang; Qunlin Jin; Shurun Li; Jiayan Teng; Zhuoyi Yang; Wendi Zheng; Xiao Liu; Ming Ding; Xiaohan Zhang; Xiaotao Gu; Shiyu Huang; Minlie Huang; Jie Tang; Yuxiao Dong | Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimensions remains a persistent challenge. Though reinforcement learning from human feedback offers promise for preference alignment, existing re... | cs.CV | 0 | Random | [
-0.8795772194862366,
-0.6468746662139893,
-0.059553083032369614,
-0.7531212568283081,
-0.3424542248249054,
-0.28921908140182495,
1.3848562240600586,
0.28941306471824646,
-0.7925804853439331,
-0.9936103820800781,
-0.0036553889513015747,
0.8362983465194702,
0.8287970423698425,
0.699081778526... | [
0.2107333540916443,
0.3004564940929413,
-0.23761901259422302,
-0.1538960039615631,
-0.34453555941581726,
-0.09032618999481201,
0.640656590461731,
-0.4264693856239319,
-0.06964550167322159,
-0.2449677735567093,
0.2996026575565338,
0.22976668179035187,
-0.07646363973617554,
-0.01619088649749... | {"http://arxiv.org/abs/2406.13743v3": 0.9586965441703796, "http://arxiv.org/abs/2412.05818v2": 0.9494907259941101, "http://arxiv.org/abs/2412.17632v4": 0.9431036710739136, "http://arxiv.org/abs/2410.14170v2": 0.9413378238677979, "http://arxiv.org/abs/2411.14193v1": 0.9399540424346924, "http://arxiv.org/abs/2410.20898v3... | 0.958697 | 0.942305 |
http://arxiv.org/abs/2412.21161v1 | 2024-12-30T00:00:00 | Open RAN-Enabled Deep Learning-Assisted Mobility Management for Connected Vehicles | Maria Barbosa; Kelvin Dias | Connected Vehicles (CVs) can leverage the unique features of 5G and future 6G/NextG networks to enhance Intelligent Transportation System (ITS) services. However, even with advancements in cellular network generations, CV applications may experience communication interruptions in high-mobility scenarios due to frequent... | cs.NI; cs.AI | 0 | Random | [
-0.8935837745666504,
-0.741507887840271,
-1.108543872833252,
-0.5758079886436462,
0.22524131834506989,
0.10183688998222351,
-0.03358113020658493,
-0.3673485517501831,
0.8634530305862427,
0.7538784146308899,
-0.17393305897712708,
0.23243071138858795,
0.41037750244140625,
-0.0837895348668098... | [
-0.01836591213941574,
0.6059951782226562,
-1.0535892248153687,
0.358733206987381,
0.17385651171207428,
0.2551249861717224,
0.1430709958076477,
-0.6432912349700928,
0.097867950797081,
0.23619498312473297,
0.3385014533996582,
-0.5783847570419312,
0.26529136300086975,
-0.22353726625442505,
... | {"http://arxiv.org/abs/2409.17681v1": 0.9396086931228638, "http://arxiv.org/abs/2412.11491v1": 0.936989426612854, "http://arxiv.org/abs/2401.17457v1": 0.9367316961288452, "http://arxiv.org/abs/2407.17186v1": 0.9352245926856995, "http://arxiv.org/abs/2411.13104v2": 0.934765636920929, "http://arxiv.org/abs/2408.03435v1":... | 0.939609 | 0.934581 |
http://arxiv.org/abs/2501.00138v1 | 2024-12-30T00:00:00 | NiaAutoARM: Automated generation and evaluation of Association Rule Mining pipelines | Uroš Mlakar; Iztok Fister Jr.; Iztok Fister | The Numerical Association Rule Mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets consisting of both features. The process is not considered as easy since it incorporates several processing steps running sequentially that f... | cs.NE; cs.AI | 0 | Random | [
-1.9522368907928467,
-0.7753439545631409,
-0.47306114435195923,
-0.5355933904647827,
-0.6547951102256775,
0.6900296211242676,
-0.20772750675678253,
0.10683058202266693,
-1.1255309581756592,
0.25261104106903076,
-0.8654980063438416,
2.2549755573272705,
-0.03509797155857086,
-0.1374394446611... | [
0.1935635358095169,
0.48619380593299866,
-0.3238157033920288,
0.3114660084247589,
-0.5667190551757812,
0.4414106607437134,
-0.1638440489768982,
-0.18685603141784668,
-0.2836720943450928,
0.4167410135269165,
-0.0023386776447296143,
-0.05593351647257805,
-0.4201883375644684,
-0.5840912461280... | {"http://arxiv.org/abs/2409.14563v1": 0.9301847815513611, "http://arxiv.org/abs/2411.00615v2": 0.9259743690490723, "http://arxiv.org/abs/2412.13350v1": 0.919266939163208, "http://arxiv.org/abs/2309.16102v1": 0.9153479337692261, "http://arxiv.org/abs/2411.17738v1": 0.9140749573707581, "http://arxiv.org/abs/2412.07885v1"... | 0.930185 | 0.915784 |
http://arxiv.org/abs/2412.20849v1 | 2024-12-30T00:00:00 | Gaussian Quadratures with prescribed nodes via moment theory | Rajkamal Nailwal; Aljaž Zalar | Let $\mu$ be a positive Borel measure on the real line and let $L$ be the linear functional on univariate polynomials of bounded degree, defined as integration with respect to $\mu$. In 2020, Blekherman et al., the characterization of all minimal quadrature rules of $\mu$ in terms of the roots of a bivariate polynomial... | math.FA; cs.NA; math.NA; 65D32, 47A57, 47A20, 44A60 (Primary), 15A04, 47N40 (Secondary) | 0 | Random | [
-2.1548449993133545,
0.5128864049911499,
0.8407719135284424,
0.4027203917503357,
-0.847713828086853,
0.8801337480545044,
-0.8349328637123108,
-0.05290350317955017,
-0.3136276602745056,
0.704767107963562,
-1.3823472261428833,
1.220947027206421,
-0.11882522702217102,
-0.6547703146934509,
-... | [
0.4834391474723816,
0.9822385311126709,
-0.9289740920066833,
0.39785417914390564,
-0.46356499195098877,
0.5200067758560181,
0.031150484457612038,
-0.4450955092906952,
0.1822483092546463,
-0.05382256209850311,
-0.34929871559143066,
0.11350129544734955,
-0.26178601384162903,
-0.1809968203306... | {"http://arxiv.org/abs/2405.03474v1": 0.9195921421051025, "http://arxiv.org/abs/2412.02489v1": 0.9169328212738037, "http://arxiv.org/abs/2411.16584v3": 0.9151487350463867, "http://arxiv.org/abs/2410.15717v1": 0.9123770594596863, "http://arxiv.org/abs/2412.14595v1": 0.9120162129402161, "http://arxiv.org/abs/2411.19250v1... | 0.919592 | 0.912678 |
http://arxiv.org/abs/2412.20771v2 | 2024-12-30T00:00:00 | Optimally Decoding Two-Dimensional Reed-Solomon Codes Against Deletion Errors | Shubhransh Singhvi | Constructing Reed-Solomon (RS) codes that can correct insertion and deletion (ins-del) errors has been the focus of several recent studies. However, efficient decoding algorithms for such codes have received less attention and remain a significant open problem. In this work, we take a first step toward addressing this ... | cs.IT; math.IT | 0 | Random | [
1.0582963228225708,
0.8168088793754578,
-1.1320213079452515,
0.463665246963501,
-0.9397228360176086,
-0.6752893328666687,
-1.7741515636444092,
2.1144843101501465,
-0.8300681114196777,
-0.4044862985610962,
-0.6169387698173523,
-1.1342508792877197,
0.7496970891952515,
-0.027394112199544907,
... | [
0.692218005657196,
0.8677254319190979,
-0.535774290561676,
0.37903261184692383,
-0.2781160771846771,
-0.018879881128668785,
-0.28696656227111816,
0.09085673093795776,
-0.35901346802711487,
0.17157985270023346,
0.21088001132011414,
-0.5313032865524292,
0.5464898943901062,
0.2077326476573944... | {"http://arxiv.org/abs/2407.07299v1": 0.9446886777877808, "http://arxiv.org/abs/2412.18430v1": 0.9347617626190186, "http://arxiv.org/abs/2411.19101v1": 0.9299657940864563, "http://arxiv.org/abs/2410.15806v1": 0.9258866310119629, "http://arxiv.org/abs/2411.08258v1": 0.9255211353302002, "http://arxiv.org/abs/2410.09031v1... | 0.944689 | 0.926702 |
http://arxiv.org/abs/2501.00093v1 | 2024-12-30T00:00:00 | Machine Learning Gravity Compactifications on Negatively Curved Manifolds | G. Bruno De Luca | Constructing the landscape of vacua of higher-dimensional theories of gravity by directly solving the low-energy (semi-)classical equations of motion is notoriously difficult. In this work, we investigate the feasibility of Machine Learning techniques as tools for solving the equations of motion for general warped grav... | hep-th; cs.LG; gr-qc | 0 | Random | [
-1.2609256505966187,
0.16341017186641693,
0.6339194178581238,
0.6435984373092651,
-0.19824545085430145,
-0.619442343711853,
1.0570220947265625,
0.5948315858840942,
0.3586723506450653,
-0.1141694188117981,
-1.1407289505004883,
0.6582846641540527,
-0.7751076221466064,
0.07811175286769867,
... | [
-0.21781374514102936,
0.8857668042182922,
-0.31073370575904846,
0.7351208925247192,
-0.12989211082458496,
-0.26995399594306946,
0.302395761013031,
-0.894034743309021,
-0.009327509440481663,
-0.5152239799499512,
0.25877052545547485,
-0.5334442853927612,
0.011249985545873642,
-0.002487083896... | {"http://arxiv.org/abs/2412.19778v2": 0.9326856136322021, "http://arxiv.org/abs/2411.09617v2": 0.9297841787338257, "http://arxiv.org/abs/2311.15940v2": 0.9090850949287415, "http://arxiv.org/abs/2412.19683v1": 0.9083141088485718, "http://arxiv.org/abs/2310.06157v2": 0.907088041305542, "http://arxiv.org/abs/2412.15405v2"... | 0.932686 | 0.909228 |
http://arxiv.org/abs/2412.20853v1 | 2024-12-30T00:00:00 | Incentive-Compatible Collusion-Resistance via Posted Prices | Matheus V. X. Ferreira; Yotam Gafni; Max Resnick | We consider a refinement to the notions of collusion-resistance in transaction fee mechanisms. In particular, we require that the collusion is by itself incentive-compatible and individually rational to all of its participants. We then study the structural properties of these notions, and importantly, characterize the ... | cs.GT; econ.TH | 0 | Random | [
-1.1795778274536133,
-0.5155478715896606,
-1.1662627458572388,
-0.3875316083431244,
0.35706332325935364,
-0.23814406991004944,
-0.4590054750442505,
1.360634207725525,
-0.10208925604820251,
0.484167218208313,
-1.6896700859069824,
0.8241475224494934,
-1.5588990449905396,
-0.03873647749423981... | [
0.21331822872161865,
0.5519484877586365,
-1.056269645690918,
0.4965973198413849,
-0.08630315959453583,
0.15733584761619568,
-0.174382284283638,
-0.12599684298038483,
-0.6457850337028503,
0.19556275010108948,
-0.5036782622337341,
-0.22721977531909943,
-0.5814908146858215,
-0.085972025990486... | {"http://arxiv.org/abs/2407.04898v1": 0.9382268786430359, "http://arxiv.org/abs/2412.15707v1": 0.9328164458274841, "http://arxiv.org/abs/2412.16384v1": 0.9325213432312012, "http://arxiv.org/abs/2412.12859v1": 0.9318416714668274, "http://arxiv.org/abs/2411.05987v2": 0.9273686408996582, "http://arxiv.org/abs/2406.03674v4... | 0.938227 | 0.928539 |
http://arxiv.org/abs/2412.21162v1 | 2024-12-30T00:00:00 | Open-Source 5G Core Platforms: A Low-Cost Solution and Performance Evaluation | Maria Barbosa; Marcelo Silva; Ednelson Cavalcanti; Kelvin Dias | An essential component for the Fifth Generation of Mobile Networks deployments is the 5G Core (5GC), which bridges the 5G Radio Access Network (RAN) to the rest of the Internet. Some open-source platforms for the 5GC have emerged and been deployed in Common Off-the-Shelf (COTS)-based setups. Despite these open-source 5... | cs.NI | 0 | Random | [
-0.36918699741363525,
-0.25017067790031433,
-1.13430655002594,
-0.2827095687389374,
0.6820694208145142,
-0.6013894081115723,
-0.3728196620941162,
-0.5989488363265991,
0.5465710759162903,
0.5368986129760742,
-0.4303332567214966,
-0.5805497765541077,
0.0781000405550003,
0.06162809953093529,
... | [
0.3620128631591797,
0.9171865582466125,
-1.1506139039993286,
0.487966388463974,
0.16548945009708405,
-0.06304265558719635,
-0.4744488000869751,
-0.8747808337211609,
0.2045932114124298,
0.4684419631958008,
0.3401874601840973,
-1.0418914556503296,
-0.08138318359851837,
0.18445104360580444,
... | {"http://arxiv.org/abs/2412.07029v3": 0.9609090089797974, "http://arxiv.org/abs/2411.18836v1": 0.9608098864555359, "http://arxiv.org/abs/2407.20652v2": 0.9555961489677429, "http://arxiv.org/abs/2412.13295v3": 0.9550808072090149, "http://arxiv.org/abs/2411.15505v1": 0.9546912908554077, "http://arxiv.org/abs/2410.18739v1... | 0.960909 | 0.95441 |
http://arxiv.org/abs/2501.03264v1 | 2025-01-04T00:00:00 | Bridge the Inference Gaps of Neural Processes via Expectation Maximization | Qi Wang; Marco Federici; Herke van Hoof | The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal performance in practice. Researchers have primarily focused on incorporating diverse structural inductive biases, \textit{e.g.} attention or conv... | cs.LG; cs.AI; cs.NE | 1 | ICLR-2023 | [
-0.4308358430862427,
-0.660481870174408,
-0.18923287093639374,
-0.9140462279319763,
-0.31074315309524536,
0.3209857940673828,
-0.16405551135540009,
0.3295767307281494,
-0.2503722310066223,
0.18561750650405884,
-0.6295879483222961,
0.9677830934524536,
0.5012036561965942,
0.5115489363670349,... | [
0.953033447265625,
0.9592868089675903,
-0.20771484076976776,
-0.46459490060806274,
-0.47992801666259766,
0.4297952353954315,
0.2468668520450592,
-0.5560259222984314,
-0.9593793749809265,
0.2971978187561035,
0.3038560152053833,
0.2837834358215332,
-0.07469720393419266,
0.018062107264995575,... | {} | null | null |
http://arxiv.org/abs/2501.05930v1 | 2025-01-10T00:00:00 | Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks | David A. R. Robin; Kevin Scaman; Marc Lelarge | We present a framework to define a large class of neural networks for which, by construction, training by gradient flow provably reaches arbitrarily low loss when the number of parameters grows. Distinct from the fixed-space global optimality of non-convex optimization, this new form of convergence, and the techniques ... | math.OC; stat.ML | 1 | ICLR-2024 | [
-1.110733985900879,
-0.3699786067008972,
0.04741794988512993,
-0.22157883644104004,
-0.8485116362571716,
-0.14248612523078918,
-0.211122065782547,
0.02515777200460434,
-0.021944750100374222,
0.3743153214454651,
-0.5268202424049377,
1.0774401426315308,
0.23666127026081085,
0.556879580020904... | [
0.18643558025360107,
1.1297366619110107,
-0.6296349763870239,
0.19651257991790771,
-0.10405360907316208,
0.4011528789997101,
0.19476483762264252,
-0.7048287391662598,
-0.29935091733932495,
0.09066536277532578,
0.3777158558368683,
0.06896106153726578,
0.22091035544872284,
0.1469947993755340... | {"http://arxiv.org/abs/2209.15106v1": 0.942939281463623, "http://arxiv.org/abs/2301.12540v1": 0.9400166273117065, "http://arxiv.org/abs/2303.02141v1": 0.9388846158981323, "http://arxiv.org/abs/2210.17216v2": 0.9345821738243103, "http://arxiv.org/abs/2206.04270v2": 0.9341133832931519, "http://arxiv.org/abs/2204.02965v1"... | 0.942939 | 0.934378 |
http://arxiv.org/abs/2501.11253v1 | 2025-01-20T00:00:00 | How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks? | Wenxuan Li; Alan Yuille; Zongwei Zhou | The pre-training and fine-tuning paradigm has become prominent in transfer learning. For example, if the model is pre-trained on ImageNet and then fine-tuned to PASCAL, it can significantly outperform that trained on PASCAL from scratch. While ImageNet pre-training has shown enormous success, it is formed in 2D, and th... | eess.IV; cs.CV | 1 | ICLR-2024 | [
-1.4609042406082153,
-0.14992143213748932,
-0.45539432764053345,
0.39077281951904297,
-1.5460885763168335,
-0.5587039589881897,
1.0420210361480713,
0.04099103808403015,
-0.013681520707905293,
-0.2840496897697449,
0.641243040561676,
0.8675168752670288,
1.3044160604476929,
0.3455723524093628... | [
0.0825771689414978,
0.5094786286354065,
-0.736258327960968,
-0.29240769147872925,
-0.3321491479873657,
0.3379586935043335,
0.32128778100013733,
-0.7409250140190125,
-0.5771634578704834,
-0.11287164688110352,
0.5673682689666748,
0.045268476009368896,
0.4455753266811371,
0.3287844955921173,
... | {"http://arxiv.org/abs/2209.15076v4": 0.945049524307251, "http://arxiv.org/abs/2308.16139v5": 0.9371547102928162, "http://arxiv.org/abs/2206.15306v2": 0.9214891195297241, "http://arxiv.org/abs/2206.15369v2": 0.9149757623672485, "http://arxiv.org/abs/2301.13155v2": 0.9122228026390076, "http://arxiv.org/abs/2308.02498v1"... | 0.94505 | 0.915913 |
http://arxiv.org/abs/2502.07551v1 | 2025-02-11T00:00:00 | Early Stopping Against Label Noise Without Validation Data | Suqin Yuan; Lei Feng; Tongliang Liu | Early stopping methods in deep learning face the challenge of balancing the volume of training and validation data, especially in the presence of label noise. Concretely, sparing more data for validation from training data would limit the performance of the learned model, yet insufficient validation data could result i... | cs.LG | 1 | ICLR-2024 | [
-0.934240460395813,
-0.9545008540153503,
0.05205864459276199,
-0.527925968170166,
-0.40168777108192444,
-0.24458234012126923,
0.543287992477417,
-0.12704281508922577,
-0.8586037158966064,
-0.513685405254364,
-0.3622809052467346,
1.140815258026123,
0.9843109846115112,
0.8912813067436218,
... | [
-0.19735436141490936,
0.6725497245788574,
-0.8650515675544739,
-0.1817561686038971,
-0.25986790657043457,
0.14546643197536469,
0.3886798918247223,
-0.17831340432167053,
-0.6462686061859131,
-0.28505659103393555,
-0.5171453952789307,
0.21410946547985077,
0.6572061777114868,
0.41648939251899... | {"http://arxiv.org/abs/2212.04461v1": 0.9264059066772461, "http://arxiv.org/abs/2210.01019v2": 0.9262416958808899, "http://arxiv.org/abs/2307.10440v1": 0.9238147139549255, "http://arxiv.org/abs/2205.09329v2": 0.9229545593261719, "http://arxiv.org/abs/2309.01614v1": 0.9200531244277954, "http://arxiv.org/abs/2202.04414v2... | 0.926406 | 0.921408 |
http://arxiv.org/abs/2502.14160v1 | 2025-02-20T00:00:00 | Efficient Inverse Multiagent Learning | Denizalp Goktas; Amy Greenwald; Sadie Zhao; Alec Koppel; Sumitra Ganesh | In this paper, we study inverse game theory (resp. inverse multiagent learning) in which the goal is to find parameters of a game's payoff functions for which the expected (resp. sampled) behavior is an equilibrium. We formulate these problems as generative-adversarial (i.e., min-max) optimization problems, for which w... | cs.GT; cs.AI; cs.LG; econ.TH | 1 | ICLR-2024 | [
-1.676241397857666,
-0.7978926301002502,
-0.27212798595428467,
-0.7983254790306091,
-0.518052875995636,
-0.19293496012687683,
0.22714479267597198,
1.1297451257705688,
-0.0870455652475357,
0.5018174648284912,
-1.5536472797393799,
0.9044691324234009,
-0.13455085456371307,
-0.0518352240324020... | [
0.2568366527557373,
0.9831135869026184,
-0.038640253245830536,
0.23084869980812073,
-0.3995283246040344,
-0.2908917963504791,
0.311123251914978,
-0.09828324615955353,
-0.4208277463912964,
0.18933749198913574,
-0.2371993213891983,
0.15808120369911194,
-0.2014896124601364,
-0.306139677762985... | {"http://arxiv.org/abs/2208.02204v1": 0.929571270942688, "http://arxiv.org/abs/2302.12780v2": 0.9273133277893066, "http://arxiv.org/abs/2307.07694v3": 0.9236817955970764, "http://arxiv.org/abs/2210.00750v2": 0.920953631401062, "http://arxiv.org/abs/2210.06718v3": 0.9207736849784851, "http://arxiv.org/abs/2301.02328v2":... | 0.929571 | 0.920767 |
http://arxiv.org/abs/2502.14205v1 | 2025-02-20T00:00:00 | Accurate Forgetting for Heterogeneous Federated Continual Learning | Abudukelimu Wuerkaixi; Sen Cui; Jingfeng Zhang; Kunda Yan; Bo Han; Gang Niu; Lei Fang; Changshui Zhang; Masashi Sugiyama | Recent years have witnessed a burgeoning interest in federated learning (FL). However, the contexts in which clients engage in sequential learning remain under-explored. Bridging FL and continual learning (CL) gives rise to a challenging practical problem: federated continual learning (FCL). Existing research in FCL pr... | cs.LG; cs.AI | 1 | ICLR-2024 | [
-1.0839312076568604,
-1.004388451576233,
-0.15253615379333496,
-1.059371829032898,
-0.6686457395553589,
0.19396139681339264,
0.4264116585254669,
0.5807656645774841,
-0.32762718200683594,
0.2733462452888489,
-0.7083421349525452,
0.9630945324897766,
-0.09540665149688721,
0.7991596460342407,
... | [
0.16652199625968933,
0.10650669038295746,
-0.5797576904296875,
0.12895774841308594,
-0.13535231351852417,
0.09132425487041473,
0.6624711155891418,
-0.13590092957019806,
-0.08675616979598999,
0.5901566743850708,
0.12116348743438721,
-0.35782092809677124,
-0.14363767206668854,
-0.26836004853... | {"http://arxiv.org/abs/2302.13001v1": 0.9690935015678406, "http://arxiv.org/abs/2309.10283v3": 0.9440291523933411, "http://arxiv.org/abs/2210.00226v5": 0.9384325742721558, "http://arxiv.org/abs/2309.07415v1": 0.9378970861434937, "http://arxiv.org/abs/2308.11333v2": 0.9358256459236145, "http://arxiv.org/abs/2302.04228v1... | 0.969094 | 0.937725 |
http://arxiv.org/abs/2503.16799v1 | 2025-03-21T00:00:00 | Causally Aligned Curriculum Learning | Mingxuan Li; Junzhe Zhang; Elias Bareinboim | A pervasive challenge in Reinforcement Learning (RL) is the "curse of dimensionality" which is the exponential growth in the state-action space when optimizing a high-dimensional target task. The framework of curriculum learning trains the agent in a curriculum composed of a sequence of related and more manageable sour... | cs.LG; cs.AI | 1 | ICLR-2024 | [
-0.7209746837615967,
-0.6611256003379822,
0.5754203796386719,
-0.5964370369911194,
-0.6297165751457214,
0.4541889727115631,
-0.17066773772239685,
1.1024218797683716,
-0.47616544365882874,
0.14627450704574585,
-1.7196803092956543,
0.42915868759155273,
-0.2894870936870575,
0.3411742746829986... | [
0.3760454058647156,
0.4752822518348694,
-0.3610532581806183,
0.23675274848937988,
-0.3597780168056488,
-0.10400505363941193,
0.4797304570674896,
-0.26903703808784485,
-0.32632148265838623,
0.44912582635879517,
0.5515142679214478,
-0.010658955201506615,
-0.6029921770095825,
0.14863832294940... | {"http://arxiv.org/abs/2305.04073v2": 0.9406986236572266, "http://arxiv.org/abs/2207.08258v3": 0.9398422241210938, "http://arxiv.org/abs/2207.05480v4": 0.9346460700035095, "http://arxiv.org/abs/2201.08115v2": 0.9321063756942749, "http://arxiv.org/abs/2210.13435v1": 0.9315920472145081, "http://arxiv.org/abs/2206.09670v3... | 0.940699 | 0.930918 |
http://arxiv.org/abs/2504.02142v1 | 2025-04-02T00:00:00 | Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds | Michael-Andrei Panaitescu-Liess; Yigitcan Kaya; Sicheng Zhu; Furong Huang; Tudor Dumitras | Group robustness has become a major concern in machine learning (ML) as conventional training paradigms were found to produce high error on minority groups. Without explicit group annotations, proposed solutions rely on heuristics that aim to identify and then amplify the minority samples during training. In our work, ... | cs.LG; cs.CR | 1 | ICLR-2024 | [
-0.9704022407531738,
-0.6856616139411926,
-0.1412714570760727,
-0.553848922252655,
-0.6539350748062134,
-0.9135361313819885,
0.475286602973938,
0.5196541547775269,
-1.0943481922149658,
-0.24077989161014557,
-0.7836708426475525,
0.8560548424720764,
0.7254350185394287,
0.7816530466079712,
... | [
0.22037532925605774,
0.6855350732803345,
-0.9616026282310486,
0.17038801312446594,
-0.8336321115493774,
-0.4267423748970032,
0.8894373774528503,
0.03164607286453247,
-0.4086061418056488,
-0.013285182416439056,
-0.007887549698352814,
-0.41375887393951416,
0.38875332474708557,
0.167669072747... | {"http://arxiv.org/abs/2309.07415v1": 0.9366135597229004, "http://arxiv.org/abs/2309.10607v1": 0.9352660179138184, "http://arxiv.org/abs/2202.11202v3": 0.9315629601478577, "http://arxiv.org/abs/2210.12873v2": 0.9311941862106323, "http://arxiv.org/abs/2309.00127v2": 0.9307069778442383, "http://arxiv.org/abs/2308.11845v2... | 0.936614 | 0.929749 |
http://arxiv.org/abs/2505.17003v1 | 2025-05-22T00:00:00 | Sufficient conditions for offline reactivation in recurrent neural networks | Nanda H. Krishna; Colin Bredenberg; Daniel Levenstein; Blake A. Richards; Guillaume Lajoie | During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagement. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In ... | q-bio.NC; cs.LG; cs.NE | 1 | ICLR-2024 | [
0.23224139213562012,
-0.3291281759738922,
-0.15502536296844482,
-0.7774963974952698,
0.1708531379699707,
0.21662500500679016,
0.31878387928009033,
0.6184749007225037,
0.05318295583128929,
0.6686518788337708,
-0.5449622273445129,
0.12006442248821259,
0.026593994349241257,
-0.063710801303386... | [
0.5215353965759277,
0.4630085825920105,
-0.1421256810426712,
0.03364589437842369,
0.1291908472776413,
0.15145565569400787,
0.4839981198310852,
-0.5947606563568115,
-0.26013508439064026,
0.05458374321460724,
0.6965211629867554,
0.8285895586013794,
0.24990962445735931,
0.14841604232788086,
... | {"http://arxiv.org/abs/2307.15092v1": 0.9290566444396973, "http://arxiv.org/abs/2209.10634v2": 0.927910327911377, "http://arxiv.org/abs/2210.01768v2": 0.9188411235809326, "http://arxiv.org/abs/2207.12316v2": 0.9151408076286316, "http://arxiv.org/abs/2301.07187v2": 0.9142911434173584, "http://arxiv.org/abs/2305.14369v1"... | 0.929057 | 0.914927 |
http://arxiv.org/abs/2506.10629v1 | 2025-06-12T00:00:00 | Task Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning | Yucheng Yang; Tianyi Zhou; Qiang He; Lei Han; Mykola Pechenizkiy; Meng Fang | Unsupervised reinforcement learning (URL) aims to learn general skills for unseen downstream tasks. Mutual Information Skill Learning (MISL) addresses URL by maximizing the mutual information between states and skills but lacks sufficient theoretical analysis, e.g., how well its learned skills can initialize a downstre... | cs.LG; cs.AI; cs.IT; math.IT; I.2.6; I.2.8; G.3 | 1 | ICLR-2024 | [
-0.8808854818344116,
-0.7796747088432312,
0.31178730726242065,
-0.6071262955665588,
-0.3327226936817169,
0.30631276965141296,
-0.20712381601333618,
0.4252113401889801,
-0.6616928577423096,
-0.05824477970600128,
-0.8945001363754272,
0.9446289539337158,
-0.15336987376213074,
0.38715267181396... | [
-0.12885573506355286,
0.5876286625862122,
-0.22934462130069733,
0.05488382279872894,
-0.3301091492176056,
-0.013563508167862892,
0.25214654207229614,
-0.003215925768017769,
-0.7077678442001343,
0.24678558111190796,
0.40124326944351196,
0.27106785774230957,
-0.40875673294067383,
0.348728299... | {"http://arxiv.org/abs/2207.08258v3": 0.9471182823181152, "http://arxiv.org/abs/2303.16194v1": 0.9460204839706421, "http://arxiv.org/abs/2201.08115v2": 0.945738673210144, "http://arxiv.org/abs/2302.13493v1": 0.9421484470367432, "http://arxiv.org/abs/2205.11027v3": 0.9402773380279541, "http://arxiv.org/abs/2210.13435v1"... | 0.947118 | 0.941001 |
http://arxiv.org/abs/2509.08609v1 | 2025-09-10T00:00:00 | Solving contact problems using Fiber Monte Carlo | Xinyu Wang; Weipeng Xu; Tianju Xue | Computational modeling of contact is fundamental to many engineering applications, yet accurately and efficiently solving complex contact problems remains challenging. In this work, we propose a new contact algorithm that computes contact forces by taking the gradient of an energy function of the contact volume (overla... | cs.CE | 1 | ICLR-2024 | [
-0.4301513433456421,
-0.6830933690071106,
0.7772231698036194,
0.4939224421977997,
-0.3725148141384125,
-0.1713043451309204,
0.23763850331306458,
0.03378956392407417,
-0.18298518657684326,
0.35871607065200806,
0.09608800709247589,
1.0680222511291504,
-0.8791394233703613,
-0.4593029022216797... | [
0.38045281171798706,
-0.001813344657421112,
-0.2748037874698639,
0.5935921669006348,
-0.3518912196159363,
0.1305651217699051,
-0.20408561825752258,
-0.5296033620834351,
-0.2153131365776062,
0.09806251525878906,
0.21769122779369354,
0.46692031621932983,
-0.13795237243175507,
-0.445475429296... | {"http://arxiv.org/abs/2305.06750v1": 0.9341107606887817, "http://arxiv.org/abs/2303.02156v1": 0.9310437440872192, "http://arxiv.org/abs/2301.05552v2": 0.930232048034668, "http://arxiv.org/abs/2303.08912v1": 0.9266535639762878, "http://arxiv.org/abs/2306.07034v1": 0.9192769527435303, "http://arxiv.org/abs/2303.03120v1"... | 0.934111 | 0.921723 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.